{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年3月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../py')\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and career='机器学习'  and monthly_salary<80000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>zhinengleibie</th>\n",
       "      <th>career</th>\n",
       "      <th>real_province</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>company_description</th>\n",
       "      <th>...</th>\n",
       "      <th>bd_flink</th>\n",
       "      <th>bd_presto</th>\n",
       "      <th>bd_heron</th>\n",
       "      <th>ml_sklearn</th>\n",
       "      <th>ml_mllib</th>\n",
       "      <th>ml_tensorlayer</th>\n",
       "      <th>ml_paddlepaddle</th>\n",
       "      <th>ml_cntk</th>\n",
       "      <th>ml_theano</th>\n",
       "      <th>ml_mahout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>117502838</td>\n",
       "      <td>10000</td>\n",
       "      <td>50</td>\n",
       "      <td>2020届应届毕业生</td>\n",
       "      <td>深度学习工程师</td>\n",
       "      <td>机器学习</td>\n",
       "      <td>None</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>集团简介：豪恩创建于1995年，是一家按照现代化企业管理运作的科技集团型公司。产业涉及汽车电...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        job_id  monthly_salary  headcount       title zhinengleibie career  \\\n",
       "431  117502838           10000         50  2020届应届毕业生       深度学习工程师   机器学习   \n",
       "\n",
       "    real_province province city  \\\n",
       "431          None       广东   深圳   \n",
       "\n",
       "                                   company_description  ... bd_flink  \\\n",
       "431  集团简介：豪恩创建于1995年，是一家按照现代化企业管理运作的科技集团型公司。产业涉及汽车电...  ...    False   \n",
       "\n",
       "    bd_presto bd_heron ml_sklearn  ml_mllib  ml_tensorlayer  ml_paddlepaddle  \\\n",
       "431     False    False      False     False           False            False   \n",
       "\n",
       "     ml_cntk  ml_theano  ml_mahout  \n",
       "431    False      False      False  \n",
       "\n",
       "[1 rows x 122 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(873, 122)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2790"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5250., 12500., 20833., 30000., 50000.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年3月中国机器学习工程师的平均工资为22609元，工资中位数为20833元，其中95%的人的工资位于5250到50000元之间。\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月中国机器学习工程师的平均工资为{salary_mean:.0f}元，工资中位数为{q[2]:.0f}元，其中95%的人的工资位于{q[0]:.0f}到{q[4]:.0f}元之间。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2020, Developers in China earn 22609 Yuan as average, the median is 20833 Yuan, 95% of them earn between 5250 and 50000 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print(f'In {year}, Developers in China earn {salary_mean:.0f} Yuan as average, '\n",
    "      f'the median is {q[2]:.0f} Yuan, 95% of them earn between {q[0]:.0f} and {q[4]:.0f} Yuan.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Functions\n",
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "    \n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >career</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031efrow0_col0\" class=\"data row0 col0\" >机器学习</td>\n",
       "                        <td id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031efrow0_col1\" class=\"data row0 col1\" >22609</td>\n",
       "                        <td id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031efrow0_col2\" class=\"data row0 col2\" >5250.000000</td>\n",
       "                        <td id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031efrow0_col3\" class=\"data row0 col3\" >20833</td>\n",
       "                        <td id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000.000000</td>\n",
       "                        <td id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031efrow0_col5\" class=\"data row0 col5\" >2790</td>\n",
       "                        <td id=\"T_84d69c46_5d5f_11ea_8f41_701ce71031efrow0_col6\" class=\"data row0 col6\" >100.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ebcc46f608>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22608.586738351256"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2790"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    873.000000\n",
       "mean       3.195876\n",
       "std        3.420376\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        2.000000\n",
       "75%        5.000000\n",
       "max       50.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2790"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >city</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow0_col0\" class=\"data row0 col0\" >上海</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow0_col1\" class=\"data row0 col1\" >27560</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow0_col4\" class=\"data row0 col4\" >62212</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow0_col5\" class=\"data row0 col5\" >603</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow0_col6\" class=\"data row0 col6\" >21.61%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow1_col0\" class=\"data row1 col0\" >北京</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow1_col1\" class=\"data row1 col1\" >25948</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow1_col2\" class=\"data row1 col2\" >7000</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow1_col3\" class=\"data row1 col3\" >25000</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow1_col4\" class=\"data row1 col4\" >48021</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow1_col5\" class=\"data row1 col5\" >555</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow1_col6\" class=\"data row1 col6\" >19.89%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow2_col0\" class=\"data row2 col0\" >杭州</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow2_col1\" class=\"data row2 col1\" >24498</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow2_col2\" class=\"data row2 col2\" >3750</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow2_col4\" class=\"data row2 col4\" >54375</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow2_col5\" class=\"data row2 col5\" >120</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow2_col6\" class=\"data row2 col6\" >4.30%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow3_col0\" class=\"data row3 col0\" >深圳</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow3_col1\" class=\"data row3 col1\" >23913</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow3_col2\" class=\"data row3 col2\" >5011</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow3_col3\" class=\"data row3 col3\" >22500</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow3_col4\" class=\"data row3 col4\" >55106</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow3_col5\" class=\"data row3 col5\" >413</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow3_col6\" class=\"data row3 col6\" >14.80%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow4_col0\" class=\"data row4 col0\" >广州</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow4_col1\" class=\"data row4 col1\" >21624</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow4_col2\" class=\"data row4 col2\" >11000</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow4_col3\" class=\"data row4 col3\" >19750</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow4_col4\" class=\"data row4 col4\" >41967</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow4_col5\" class=\"data row4 col5\" >322</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow4_col6\" class=\"data row4 col6\" >11.54%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow5_col0\" class=\"data row5 col0\" >南京</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow5_col1\" class=\"data row5 col1\" >20411</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow5_col2\" class=\"data row5 col2\" >8210</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow5_col3\" class=\"data row5 col3\" >20000</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow5_col4\" class=\"data row5 col4\" >38850</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow5_col5\" class=\"data row5 col5\" >142</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow5_col6\" class=\"data row5 col6\" >5.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow6_col0\" class=\"data row6 col0\" >成都</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow6_col1\" class=\"data row6 col1\" >16860</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow6_col2\" class=\"data row6 col2\" >5267</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow6_col3\" class=\"data row6 col3\" >14167</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow6_col4\" class=\"data row6 col4\" >34917</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow6_col5\" class=\"data row6 col5\" >108</td>\n",
       "                        <td id=\"T_6336941c_5cc0_11ea_a4b6_701ce71031efrow6_col6\" class=\"data row6 col6\" >3.87%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19230442088>"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_col(data,'city')\n",
    "data_pl=data_pl[data_pl['head_count']>100]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18888888888888888"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].sum()\n",
    "other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['city'].values)\n",
    "labels.append('其他')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.1f%%')\n",
    "plt.title(f'算法工程师去哪里')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow0_col1\" class=\"data row0 col1\" >go</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow0_col2\" class=\"data row0 col2\" >28429</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow0_col3\" class=\"data row0 col3\" >29154</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow0_col4\" class=\"data row0 col4\" >6311</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow0_col5\" class=\"data row0 col5\" >55639</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow0_col6\" class=\"data row0 col6\" >197</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow0_col7\" class=\"data row0 col7\" >3.81%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow1_col1\" class=\"data row1 col1\" >r</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow1_col2\" class=\"data row1 col2\" >27837</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow1_col3\" class=\"data row1 col3\" >27500</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow1_col4\" class=\"data row1 col4\" >9950</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow1_col5\" class=\"data row1 col5\" >58333</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow1_col6\" class=\"data row1 col6\" >176</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow1_col7\" class=\"data row1 col7\" >3.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow2_col0\" class=\"data row2 col0\" >4</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow2_col1\" class=\"data row2 col1\" >scala</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow2_col2\" class=\"data row2 col2\" >26795</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow2_col3\" class=\"data row2 col3\" >27500</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow2_col4\" class=\"data row2 col4\" >7500</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow2_col5\" class=\"data row2 col5\" >50000</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow2_col6\" class=\"data row2 col6\" >82</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow2_col7\" class=\"data row2 col7\" >1.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow3_col0\" class=\"data row3 col0\" >6</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow3_col1\" class=\"data row3 col1\" >matlab</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow3_col2\" class=\"data row3 col2\" >24239</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow3_col3\" class=\"data row3 col3\" >22500</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow3_col4\" class=\"data row3 col4\" >3750</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow3_col5\" class=\"data row3 col5\" >60722</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow3_col6\" class=\"data row3 col6\" >228</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow3_col7\" class=\"data row3 col7\" >4.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow4_col0\" class=\"data row4 col0\" >7</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow4_col1\" class=\"data row4 col1\" >cpp</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow4_col2\" class=\"data row4 col2\" >23474</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow4_col3\" class=\"data row4 col3\" >20833</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow4_col4\" class=\"data row4 col4\" >5250</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow4_col5\" class=\"data row4 col5\" >58333</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow4_col6\" class=\"data row4 col6\" >1548</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow4_col7\" class=\"data row4 col7\" >29.95%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow5_col0\" class=\"data row5 col0\" >8</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow5_col1\" class=\"data row5 col1\" >python</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow5_col2\" class=\"data row5 col2\" >23377</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow5_col3\" class=\"data row5 col3\" >22500</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow5_col4\" class=\"data row5 col4\" >5250</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow5_col5\" class=\"data row5 col5\" >58333</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow5_col6\" class=\"data row5 col6\" >1827</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow5_col7\" class=\"data row5 col7\" >35.35%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow6_col0\" class=\"data row6 col0\" >10</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow6_col1\" class=\"data row6 col1\" >java</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow6_col2\" class=\"data row6 col2\" >21561</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow6_col3\" class=\"data row6 col3\" >20000</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow6_col4\" class=\"data row6 col4\" >3803</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow6_col5\" class=\"data row6 col5\" >47333</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow6_col6\" class=\"data row6 col6\" >812</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow6_col7\" class=\"data row6 col7\" >15.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow7_col0\" class=\"data row7 col0\" >13</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow7_col1\" class=\"data row7 col1\" >php</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow7_col2\" class=\"data row7 col2\" >14988</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow7_col3\" class=\"data row7 col3\" >8412</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow7_col4\" class=\"data row7 col4\" >5250</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow7_col5\" class=\"data row7 col5\" >41161</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow7_col6\" class=\"data row7 col6\" >83</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow7_col7\" class=\"data row7 col7\" >1.61%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow8_col0\" class=\"data row8 col0\" >14</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow8_col1\" class=\"data row8 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow8_col2\" class=\"data row8 col2\" >10850</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow8_col3\" class=\"data row8 col3\" >8267</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow8_col4\" class=\"data row8 col4\" >5250</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow8_col5\" class=\"data row8 col5\" >27646</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow8_col6\" class=\"data row8 col6\" >133</td>\n",
       "                        <td id=\"T_a189f96e_5cc0_11ea_8a63_701ce71031efrow8_col7\" class=\"data row8 col7\" >2.57%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19231d51f48>"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "data_pl=data_pl[data_pl['head_count']>50]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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31qo1dwNOm+MIEUn+C9y2bNXiE3YHEfaSwjiABGac/hGYYXcWISJUHfBpmbk6sElhHABKS8oU8EVgBZBgcxwhosGvsBYG6LA7iOh/UhhjXGlJ2SCsUeKVdmcRIsrsBm5ZtmrxTruDiP4ly3zFsNKSsouBrUhRFKI3JgEbSkvKPm93ENG/ZMQYo0pLyr6KdenUZXcWIWLA01gTc1rsDiLCTwpjjCktKUsFfg982O4sQsSYPcD1y1YtPmh3EBFeUhhjSGlJ2XjgX0CR3VmEiFENwNJlqxa/YncQET5yjzFGlJaUXQtsQIqiEOGUCbxQWlL2ZbuDiPCREWMMKC0p+xZwP6DsziLEAPIY8JllqxZ77A4iQksKYxQrLSlzAb8BPmFzFCEGqnLgBtmpI7ZIYYxSpSVlKcA/gKvtziLEAHcSqzhusDuICA0pjFGotKQsD3gOWdpNiEjRDty0bNXi5+wOIvpOJt9EmdKSskJgHVIUhYgkScC/SkvKPml3ENF3UhijSGlJ2QJgLTDS5ihCiPdyAb8LTIYTUUwKY5QoLSn7EPBvIMvuLEKIc1OmUbJl6oIVFYVFMks8SklhjAKlJWWfBf6O7IwhRGTTZt3szSu8iZ76bwC/rygskj1Po5AUxghXWlL2BeCXyJ+VEJFN69bp2x6uTmk7OSrwyO3A3ysKi+LsjCV6Tt5sI1hpSdkXgYftziGEuACtvRftfmR/ZtOBiWd95Qbg2YrCoiQ7Yp2LUmqRUmpkF4+vucDrHu3qdbFGCmOEKi0p+xLwM7tzCCEuQGtz/MEnNg+q3XaumeJXAi9VFBYl92esC1iETOI7J+ljjEClJWV3AT+xO4cQ4sKGH3v5tbGHn760G0/9D3Bt0d6KsC0hp5TaDJwBvEAe1nZZ7wc08JrW+h6l1O+By4BGYLfW+qNBr1+jtV4U+HwI8MRZr30USAQKgA1a65hcM1ZGjBGmtKTsK0hRFCIqDK7euKabRRHgcuCJisKicO6RmgR8BJgC3Io1Wr0beB/wAQCt9SeBR4G7gotiF4ae/dqA57TWC4CJSqlpof4GIoEUxggS2Fx4pd05hBAXltF44NVJFY8u6uHLrgP+UFFYFK733mqtdStwFDCAZqzi9giQ2sNj+c/x2vLAr1uAMX1KG6GkMEaIwEjxR3bnEEJcWFLbqbXTt/20uyPFs90CrAplnvNoBh4C7sC6JPqWDqzRJUqpc/VbfuUcr50Z+HUKUBnCrBFDCmMEKC0p+zgyUhQiKsR5GjfN2fTgHNW3bd7+p6Kw6MchC3VuN2IV4dVAu1JqaODxJ4G7lVLrOfeo79lzvPZGpdRa4IjWenP4ottHJt/YrLSk7Gqsv4DhvO8ghAgBl69954J194xxmt5QtV/cX7S34t4QHUuEiBRGG5WWlM0C/guk2J1FCHF+DsN7cMG6e3Lc/vaMEB/660V7K+Q2SgSRwmiT0pKy0Vi7ZAyyO4sQ4vyUaVTNK7/XmeBpyA/TKT5btLeiv+47iguQe4w2KC0pywVeQoqiEJFPm7VzNj3oD2NRBPhlRWHRDWE8vugBKYz9rLSkLBlrk+GxdmcRQlyA1i0ztv6kJrn99Mgwn0kBf6woLJJ9ViOAFMZ+VFpS5sLaJWO23VmEEBegtWfy7l8fzGg+XNRPZ0wCVlcUFg3pp/OJc5DC2L9+jrWKhBAikmltjD/wt625tTum9/OZhwLPRNqi4wONFMZ+UlpSdgdQYncOIcSFjTz64psFJ1+fa9PpZ2BdVpWNjm0ihbEflJaUzQF+YXcOIcSF5Z0uXzO68tlLbI5xA/AdmzMMWNKuEWalJWWDgM1Yq9ELISJYZsO+V6dvf3ih3TkCNHBD0d6Kp+0OMtDIiDGMgibbSFEUIsIlt55cO237w71d/zQcFPB4RWFRf03+EQFSGMNrJRBJ/9CEEF2I72zYMHvzQ8V9XP80HFKBpysKi9LtDjKQSGEMk9KSso8BX7Q7h+idts5mKqo20drRdMHnNrfXYxj+fkglwsHla9sxd8N9kx3ajNT1iscBj9sdYiCRwhgGpSVl04Ff251D9E67p4VVL97D0TP7+NmzX6Wlo5E/rfkhP/rn53lxyx8BeHXXv/jBU5/D4+ugomoTTmekvqeK83EY3v3zyu8d4TR9iXZnuYAPVBQWLbM7xEAhhTHESkvK0rC2dIn0f2jiHE7UHeZD8z7LNTM+SlHBLPaf2IqpTb52wy+obT7FmaYqquoOMr/wfRyt2UecK8HuyKIXlGkcn1t+X7rb3xEtlyl/VFFYNMnuEAOBFMbQ+zkwyu4QovfGDZnKqMETOXhyB0fP7KWiahMzxiwCoLBgJodO7QKtMUyDvcc3MWnYHHsDi57TZs2cjd8zE7yNg+2O0gMJwF8qCovi7Q4S66QwhlBpSdlHgI/bnUP0ndaazYf+S1J8KgpIT84BIDk+jZaOBgoLZrHr6HoyUnL5fy99m/0nttobWHSf1k0zt/64PrmjeoTdUXphMvB9u0PEOimMIVJaUjYEa7drEQOUUiy95EsMzR7N4eo9+PweADy+DrQ2mTn2Mt4/6+MkxqUwaXgx2468bnNi0S1ad07ZtaoyvfnIBLuj9MEXKwqLrrE7RCyTwhgCpSVlCngUyLI5igiBV7b9hfL9LwPQ7mnlymk3c+j0LgBO1B0iKzUPgJqmE+SmDcHldCMLZUQBrY3C/X/enlO3a6rdUfpIAY9WFBbJtnVhIoUxNL4EXGl3CBEaC4qWsGH/K/zk6bvQ2mTqyAVs3P8KT775S7YcfpWLhhfT4W0jNSmTvMwRrK14jglDZbegSDeq8vl1Q069WWx3jhAZDPzO7hCxSpaE66PSkrKLgI1YN8ZFjGr3tLC3ajNj86eQliQXBqJN/qm1a4r2/XmR3TnC4ItFeyt+bneIWCOFsQ9KS8rigQ3AFLuzCCG6llW/Z820HaWL7M4RJp3AnKK9FTvtDhJL5FJq39yPFEUhIlZKy/E3YrgognWl6rGKwiKn3UFiiRTGXiotKZsKfMXuHEKIriV01pXP3vz9eXbn6AfTgS/bHeJclFJr7M7QU1IYe6G0pMyB1Zoh64AJEYHcvtZtxRvun6rQA2UkdV9FYdFIu0PECnlj7507Abt29xZCnIfD8Oybt/7e0U7TP5AmxCUBvwLeF+oDK6USsbbPSwPqgNuAR7C202sEbsIaZP0DSAYOaq0/eY5jpXTneXaTEWMPBTYefsjuHEKI91Km/+i88uVZLqMzze4sNrimorDoljAcdyJgaq0vBX6PdQtpu9b6Yqx1oS8C8rGWw7wCGKmUOtdSe919nq2kMPbcj4EMu0MIIc6izerijQ844r3NuXZHsYOpOPP7Kxy3T35scqgXRd8C7FJKvQxcDeRhzcYHa2GTjYAPuAP4E9ZCJ+faRKG7z7OVFMYeKC0puxz4qN05hBBn0bpp1pYfNid11AyzO0p/02DuLeC1T93ljH9htuNq4IEQn2IqsFZrfRWQCewDZge+9k2sQvdprEuktwBt5zlWd59nK+lj7KZAz+IOYLzdWYQQQbTumLrzlwey6/cMuNapDjcVK25y6orhamLQwwYwe+ftO0Oysr1SKgP4G5CK1Td5A/AbYBDWPcePAnOAXwINgBP4utZ6beD1a7TWiwKfX3qu50USKYzdVFpSdi+w3O4cQoggWvuL9j6+Nb+6fPaFnxw7NDQ/P0tt+8MVjou1Ul1d+VsPzN95+055g+8FmZXaDaUlZSOA/7M7hxAiiNZ69JHV5fnV5QvsjtKfTmax7v5bnKPr09Sl53naXKyR3B/7KVZMkXuM3fMAIJuDChFBhp58/bWRx14eMEXR7+DoT693bL7rM6559Wndms354OTHJkfk5JZIJ4XxAgIr3Nxqdw4hxDuya3eumXDgbwvtztEfNHjWT1Brbv+qc/CbEx0ze/DSYUTwijiRTArjha1Afk5CRIzU5qOvT921apHdOfpDUxJbvnqH8+SPP+Rc5HOp3ixYcPfkxyZHZK9gJJN7jOdRWlJ2GSA7ZQsRIRI6atfP2vLD+XbnCDdTUfOnRY4Dz8x19PV7TQW+AywLQawBQ2alnkNpSZnCamKdZXcWIQS4vS1bF6z71kSH9sfs/X4N5r6hvLHiJufU9gQVqkZ9HzBx5+07D4boeDFPRozn9hGkKAoREZyGp2Je+b1jY7kodrqpWPERp94z4ryzTXvDjTWB8OYQHzdmyYixC6UlZS6gAhhrdxYhBjpl+ivnr/tWcryvJSaXetPQ/MIstfUPlzsuNh0qXLuBaGDWztt3bgnT8WOKjBi7didSFIWwnzZPz93wXVesFsWgnsRwz7BVwL3A9WE+T0yQEeNZSkvKEoAjWAvlCiHsonXj7M3fr01tPR5z/0n1Ozj6iw84anvYfhEKM0K1VFwskxHje30aKYpC2Evr9mk7fnE8tfX4ZLujhJIGT/kEte7n1znm+lxqhA0RvoO11qk4D+nPCxK4t/h1u3MIMaBp7Zu497E9WQ17Y6oohqAnsW+09k/weLOb78+IqZ9rOMiI8d0+CtjxvzghBIDWeszhf23Iq94YM0u9hbAnsXe09l7k8ZavqKkbOcLvvwRr3WdZzes85B5jwMqlS1R8xpe2KeUccFvXCBEpCqrWvDr+4N9jYqm3MPUk9iCA7pjZ6dn4YG3duCF+Iz/oKwYwgeVNh/o9U5SQEeM7rvM0PpzjjJ/yqivxkplKxaXYHUiIgSSnZtua8Qf/vsjuHKEQxp7EC9O6dUFH56bv1tZNyjXMrs7vBP4X+Ew/J4saMmIMWLl0yevAxYHfNjjcY7e7k66YrBxJ2XbmEmIgSGs68tqsrT/q/yISYhqaX5yptj52RVh7Es9xct10eXvHtntr6ydnmmbWBZ7tAUayvOl0f0SLNlIYgZVLlxRjbex5tg7lGrLRnXT1aIczs6C/cwkxECS2n1k3d8P9xQrdp8mAtX4/6U4nbqVCFa1HTmay7ru3OkfVpal+ndWutK5/X1v7znvq6qelmbonl2yXs7zpvrAFi2JSGIGVS5f8HbjxPE/xK0fWBnfy1TkOV/74/solRKxze5u3LFj3rYsc2ojryetq/X7urDrOh9MzeLq5iUeHDeeVlhauT+//W3l+B0dLlzhq1k5y9OsSkkrrmg+1tO353/qGWUlaJ/fiEKeAESxv8oU6W7Qb8IVx5dIlo4H9WNfdL0wlb3InXeF2xo2ZGtZgQsQ4p79zz4J13xzuMjw9vp//jVMn2dXZyYzERC5KSGBUXByNhsFVqWnhiNolDd4NE9S6h69zFPdn+4VD61O3NLfs/3JDY3G8pq/nvZnlTX8LSbAYIpNv4PN0tygC6LZZvran8bXH7XIlXtrmjJs8Rymbrt0IEaWU6Ts8r/zewb0piuvb2khUDnKcTjTg17C2rZ2S7P6bDtCUxJb7bnVmVuWGfSm3t7m0Pv6JpubKzzY0FcdBqM77eUAK41kG9Ihx5dIlccBJoA//opyHXAlzTjkT5hQr5XSHKpsQMUsbp+aVL9eJnfVDevpSr9bcefw4Dw8dyhdOVHFzRib/bGrispQUXmlt4TPZ2RQn9eaqYvcEehL3PzPX0W99lm6tj3ymoenkp5uai13hGcxMY3nT9jAcN2oN9BHj9fSpKAIYY/yd68b4O9efcsZN3u9KulRaPYQ4F63rZ2/6fntiZ/2Y3rz8kbo6bs7MIM1pXeR5X1oaQ9xujvu8LExO4ZWWlrAURg3m/qG8seIjzqltiapfimK8aR74YkNT7ceaW4odMCqMp/o88D+hPKBSag2wEZiitb46lMfuDwN9xPgCcE2ID9vocI/Z5k66YpJyJMfkjgBC9IrWbdO2P1yZ1bh/Um8Pcduxo2+vY7nX4+Gq1FRmJyYxIi6OPZ2dHPB6+M7g0E4KDepJnBjSA59DkmlWfK2+ofnGlrY5ytoVI9zagQKWNzWE6oBKqU7gVq31U6E6Zn8asIVx5dIlBcBRwrdebIdyDtnoTr56lMOZOSxM5xAiOmjtnbTn9zsH12wO2W4Stx87SunQAnZ0djI1MYGPHTvG57JzuDI1NSTH7++exBTD3HlPXb1nSVu7HRukf43lTStDdTCl1CatddRu9D6QC+O3gO/2w6kM5cgsd1C+6RsAACAASURBVCdfk+1w5U/oh/MJEVm0NsceenL98Kr/2rNWaC/0Z09ipmFs/U5tvbqivWNauM91HvtZ3hSy9yel1Bqt9aJQHa+/DcjCuHLpEgUcAHp1n6PXVPImd9LlLmfcWDv/AQjRr4Yd//dr4w79MypWtenPnsRcv3/T/bX1cRd3dEbK+szzWN7U1UInPRbthXGgTr5ZSH8XRQi0eqzG1xa325V0SaszbspspZRs/SVi1qAzm9eMO/TPRXbnuJCzehLDt8OO1nqI39j4vdq6lFmdnki71Hg7Xa8A1mPRXBRh4I4YHwc+ZncOcB5xJsyuciUUFyvl7NHKH0JEuvTGg6/O3PaTiN8poymJrfff4sw4PkiFb+an1uYIv7/8oTN12ZO93khdPasByGd5k8fuIHYbcIVx5dIl6VhLISXaneUd6rQz7qJ9rqRLZygVH5qZA0LYKKn99JvFG747r59mVfaKqaj58yLHgdXh3CdRa/84n6/8oTN1+RN8vtFhO0/ofITlTf+wO4TdBuKl1FuIqKIIoPMM7848w7uzyeEe/ao76cqJ0uoholWcp2nTnI3fmx2pRfGsnsTwFEWtvZO83vIVZ+pGjPT7o2nT5VuBAV8YB+KIcT1QbHeOC+hUzvyN7uSrRzicWcPtDiNEdzn9HbsufvObo5ymN3zLz/RBp5u93/+Iw9g9wtHrXsrz0rpzhsez4cGaurFD/UaPV/aJAB5gMMubmuwOYqcBVRgDvYvH7c7RA4FWj6uzHK4hhXaHEeJ8HIbv0IJ192S5/W2Zdmc5m4aWQE/igrD0JGrdNq+jc9MDtfVFgwxjUMiP378+xfKm39sdwk4D7VLqdXYH6CGnNhvme1v+Cippszvpcoczbtx0u0MJcTZlGieKN9yfFIlF8VQm6+63ehJD3zKidfNl7R1b7qutn5JpmhE/0aibbgEGdGEcaCPGF4GoW7fv3dx7XImXNDvjp86RVg8REbRZN2fTQ80pbSfDuZ5nj52vJ9Hf5MeZ7ES5encbVGndcE1b+/Z76uqnp/dsc+Bo4AdyWd7UaHcQuwyYwrhy6ZJUoBaIkbYIR6DVY660egj7aN06fdtPj2U2HeyXdUS7Q4N3w3j15k+u1HMP/6YqQZsaR7yDlKIUGt9sZNTdo2ja1ETmgp4PbpXWNR9sbdv9jbqGWclax/JmAbewvOmvdoewy0C6lHoNMVMUAcxRRmf5KKNzQ7UzbtJeV9LC6UrF998urUJo7blo9yP7M5sOzrA7yluCehIX1f2njpyrc0i5KIWTj52k9uVact+fS/vhdhxxPbvY4tD61NLm1gNfaWicnRDlzevd9AFACuMAEG33F7tJDza8uwYb3l1NDveoNe6kKycpR4q0eojw0tocf+CJrYNqt821Owp03ZOYffk7O8r5W/zE5cShDU3r7lYGfaB782OcWlfd3tRyZFlD45w4iIpl7ULkfSxPd7G8yW93EDsMiEupK5cucQFngIibGBAGncqZt8GdfM0IhzMrfEtbiQFtxNGXXh9zZPUlduc4qyexy3t97QfbqX6ymqxFWTS83kDq9FSaNzeT+4FcUoq6vhrq1rryzsamqjsam+eGaXPgaLCI5U2v2h3CDgPlD/xiBkZRBEjQxulLvc2PGsqRsc6dfHWGwzW0yO5QInYMrt746pgjq22fgRnUk3jOkZy/1c/JP55k+OeHE5cThzvbjbfGS+rUVJo3Nb+nMMab5sHPNzSdua25pdgJI8P9PUS4DwBSGGPY9XYHsIFTm43zvC1/A5W0xZ20GGfc+Ii5FySiU0bjgVcnVTxqa1Hsbk+i6Tc5XnqcvBvziMuxphd4qj3E58VjtBsQdLEs0TQrvlrf2HxTS+scBWPD/k1Eh+uAr9kdwg4D5VLqISAa1ikMM/ceV+LFTc74acXS6iF6Kqnt1NrijQ/Mt3Opt6CexAvuk1hXVkf1P6pJGJYAQOYlmbgz3CSOSeTIg0fIvT6XoTNSd/1fXUPHda1ts8MePjoVsrxpn90h+lvMF8aVS5dcBOy0O0dkcVQ6E2YddyXMnaOUK97uNCLyxXkaN81f/+2pDm267Ti/38GxXy5xnHkjRPskphvGtu/U1uur2jtkwYzz+xrLm1baHaK/DYRLqVHe0B8O5kijc8NIo3PjGWdcUYUrcdF05UiQVg/RJZevfee88vsm2lEUAz2J6x6+3lHsc6k+rxuc4zc231db57q0o1M2C++eKwApjDHoYrsDRC49yPDuGWR49zQ7XCNfdSdfVaQcKdG+zqMIIYfhPTCv/N5hTtOb1N/nbk5k6323OjOOD1J9vqeZ5/dv+F5NXfKcTs/MUGQbQC4eiG0bA6EwRtOWL3ZJM/2VCz1Nv/Yo5+DX3cnXDHM4s0faHUrYS5lG1dwN96W6/e0Z/XleU1Hzl4WOA0/P6+M+iVqbw/3+8odq6rKmeLxzQhRvoEkBZgHr7Q7Sn2K6MK5cumQ8IM3u3RevjepLvM2PmcqRvs6ddHW6w10QMUt9iX6kzZo5mx70J3gaC/rtlGAeGMLah25yTunTPolaG2N9vvUP1dTlFXp980IYcaC6DCmMMUUuo/aOQ5tN87ytT4BK3OpOWmw64ybIJaiBQuvmGVt/Upvcfrrf+l+DehJ7v2iA1r6JXm/5ipq6YaN8UbU5cMTRmtYaMva+YV7U9rSxYPxjdgfqZzE9K3Xl0iW/BT5ld47Y4K5wJS5odMZPm6OUI/T72YnIoLVn8q5fV+TW7eiXySmBnsQtj13huLjX+yRq7Znm8ZY/VFM7psBvDA1xxAHB1DSeInv/q8bUjqeN+bkbdeEEk7f/nTcDmZUrrjXtzNifZMQouslX5O9Yg7/jtaPO+BnHXInzZyvlSrA7lQghrY0J+/+6NbduR7+sfxrUk9i7yTVatxd3ejZ+r6aucLBh9Hgd0/oOzeaTBtPzHeQkvdPWW91qkpWocDtta9cMO1OrmmN60KEyc7p3tTE/b7sePVbjONd92DRgCrCtHyPaKmYL48qlS3KB8XbniD3mCMOzaYTh2VzjiCta77ZaPWJtP7oBaeTRF98ceuqNsK9/GtST2Lv7f1q3LOzo2HxfTf1F2b3cHLihQ7Pkz+1cO87FV17u5KaJblbv97Hm9mReOuTn41NjaCMewK8dpw7r/MOvmLPMZ4x5BXv18FH0bP7FAqQwxgS5xxBWOtf07lnksVo91riTryxSjtTBdqcSvZN3ev2a0ZXPLgrnOfrak6i0bryqrX37t+sapqab5qK+ZNlRbfDjq+OZW+CioVPzu21e7l4Qz8aTBsnu6B8p+rTz6H5dcPxFY7Z6xpw3vFLnDwPy+3DIi4HSEMWLeFIYRV+lmf7KRZ6m3wRaPa4ucDhzImond3F+mfV7X5249/FF4TxHX3oSlda117W27b67rmFGitYhWad14Ujrre+1o342nDBYNNKFz4SXD/n51qXRtxiUR7sP7dYjTjxvFLueN4pHnSRnBBDK3XUiYnux/hKzk29WLl2yjgH2hxkhTOVI3+BOuirN4R4mrR4RLrn1xBtzNj24IFzrn/alJ9Gh9emPtLTu+2p94+xErUO+wIDWms8/30lVi+ZDhS7+ssvHdRPcPFXh455L4rlsVGSOG7TG7CD+wA49uvpZY27cC8acsXWk5/TDqdMrV1zb3A/nsV1MFsaVS5ckAE1AbN0oiDYqcas76TLTGVcorR4RKL6zYcO88u/McGgz5BXgrJ7EHt2DdmpddVtTy+EvNDQWx0HYh2/fLuvkokFORmQoDjdoqltNDjWY/OL9ieE+dbdojb+VxH2bzXG1z5rzkl42Zo5vJsWO+/qXVK649g0bztvvIvO/RH1XhBRF++mO6b625/G1vbLXlTi/wRk/XVo9IoTL17Z97ob7JoejKHa62feDGx3+XSN71pPo0vro/zQ2H/+fxqZiN4R1YYHvv+EhP1Xx8alxNHZqMhIUB+pMxmc7aOxUmDaOF7TG00jyvg1mUf1qY37af81pE9pJmGRfordNBaQwRjHZmDei+Ar9Ha/i73j9mDN+RqUrcf4cafWwj8Pw7p+3/t6RTtMX0iGRhpaXZqitj155/n0SzxZn6kPLGhurb29qKXaG9r7YOd05M46b/tHOI1t8XDTIwdwCJxtOwMRcJ595tpPvLOy/+4xa01ZL+r43zYktTxsLMt4wJxd6cU/ptwDdF4mZwiJWC6Pc24pI5nDDs2m44dkUaPW4TFo9+pky/cfmlS/PcBsdIf25n8pk/f23OEfUpatu9xMmmOa+r9Q3Ntzc0lqsYEwo81xIZqLilduS3/XYlWOst8NtJSlhPbfWNJ0mc/9rxpS21eaC3HKzcLwfVzRsIj5gCmOs3mN8EviQ3TnEBbU4XMO3uJOvGq8caX2ZSi66Q5tnijc80JHcUR2yUZnfwbFfXes48/pF3d8nMdk0d3+jrqH9hgGyObCpVV2Vzjn4X3Oa52ljwaCteux4jSMaNwpvA1IrV1wbe0XjLLE6YpRLqdEh1fQfW+hpesSrnINedyddPdThyh1td6iYpHXTzC0rG5I7qieE5HDg3ThOrXv4esccr7t7PYlphrH923UN/mva2mN6Mpah1elKnXf43+ZMY7Uxb8huPXI0qGK7c4VAMtbI/qDdQcIt5grjyqVLXMBYu3OIHonTxplLvC2Pa+VIK3clXZXidA+PhMkGsUHrzim7VlWmt1RODcXhmhPZev+tzvRj3exJzLY2B3Yv7OgMyfkjjU87qw7qIUdfMmebzxjzRhzSQ4cDeXbnCpMpSGGMSmOBft9pXISE0mZzsa/1H/hUwnZ34mU+Z3xRty/RiS5obRTu+9OOnLpdfd6PMNCTuP/peY5uLZ6R5/dvfKCmLqk4xjYH9mjXkb16eNULxhzns+a8kVU6t4Awz6KNIFOBp+wOEW6xWBjlMmos0J1Tfe0v4Gt/ZZ8rcX6dM35GsbR69JDWelTlc+uGnF7Xp8X0Az2Jbzx0k3NqW6I6f1HUWg+zNgfOnOrxRv09RK3RncQd3KlHnXzOmBv/gjFnzBkyRwEDdXWni+wO0B9isTDKjNSY4p/g73gNf8cbx53x0464EhfMVsodGZ3XEW7IqTdfG3X0hT4toRbUk3j+2aZaG6N9/vIVNbWDi7y+qF1xSmuMNhL2bTXH1jxjzkt82Zg1rpHUccA4u7NFiH6dPWyXWCyMMmKMSeYww7NlmOHZUutwF5a7ky6bphyJGXanilRZdbvXFO7/86Levr7bPYla+wq9vvIVNbUFY3z+Hi/7Zjet8TaTtG+jOaFutTE/9T/mjPFtJMp/rs8tZCNlpVQi1mXZLOAQsA8oBIYAVcAntdbeUJ2vR9lirV1j5dIlm4Fo6AkSfdPqcA3f7E66arxySqtHsJSW46/P2byi19tHBfUknvvnqrVnqsdb/lBN3ehhfn/U3F/Tmo56UvetNyc2PW3MT3vNnFLYSbxcgeiZ7MoV19b39SBKqenA5wIfa4E/AOla6+8ppZYDp7XWq/p6nt6IxRFjSKaji4iXYvqPLfQ0P+JTztw33EnXDJFWD0joqCufvfn7vRq5BfUknvtSqNbtc6zNgSfk9WJz4P6mNS1nyNj3hjm57WljftY6c9IEH65pdueKcqOAPhdG4AQwE3gN+BnWjkhvTexZD7wvBOfolZgqjCuXLsnE6rURA4dbGzUXe1se1zjSyt1JVyY73SMGxASBs7l9rduKN94/VaF7NEmpWz2JWrdc0tG55f7auok5Ru82B+4PpqbhJDkH1hhTO1Yb8wdt0hPGm3R/8QHRLaOAzSE4zjXAd7XW/wRQSmVg7Yj078Cvu0Nwjl6JqcIIyEa5A5fCbC72tT4ZaPVY5HPGTxwwb4gOw7N33vp7RztNf4/WoL1gT6LWTVe2d2z7Tm39lAwz8gqioVXNMT344H/M6b7Vxvz8HXr0WFB9bk0R59XjTabPYSvwglLqC8AZ4CHgbqXUa8Bx4MEQnafHpDCK2KM7p/raX8TX/u/9rsR5Nc74mcVKOWLt7/rblOk/Om/9vdkuozOtu68xFTV/vdSx/1/zu+5JVFrXLWlt2/l/dQ0zUkO0OXAo+LXj5CE95Mgr5kzzGWNewT49fBSQa3euASZU95TnAvsBH5ACZGqtbwnRsfsk1t4spDCKIP7x/o7Xx/s71lY546cdciUumBNzrR7arC7e+IAj3tfSreKgQR+0ehInt3bRk+jQ+syHW1orvm5tDrwo5Hl7yKudR/frYcdfNGarZ8x5I47qvAKsWYvCPiEpjFrr3wC/CcWxQk0KoxgAzALDs6XA8Gypc7gnbHAnLZ6iHImZdqfqM62bZm35YXNSR023euzOt0+iU+uTH21uOfjFhsY58RpbRohaoz24D+3WI08+bxS7nzeKR50iewT9tBXV+RhtDTgSUlHOWHvL7JVhdgcIt1j7U47V9QlFaGSbvn0LPU372hyuYa+6kq4a53CmR+foQ+uOqTtKj6a1HLvgVkAaWl+eoTb//krHxWf3JLq0PvqpxubjJdbmwP36s9Aas534/TvMMWeeMefGvWjMHldP+lhsXuvYaGug+ol7SZlyJW27yhh8y0N0HNlCykWX2xkrkkRNe05vxVphlBGj6I5k0398obf5tz7lzHnDnXRNvsM1KHpW9NDaX7T38V3ZDRUXXHLtdAbr77vVOaIu/d2Ta+K0PvzZhqZTn2xqnttfmwNrjb+FxL2bzfF1zxjzkl4xZ45vIbkQq6k7YjT893dovxffmSOkTL0K7+n9KLfsqx0k2+4A4SaFUQxkbm3UXuxt+aPGkbrBnXRlotM9crLdoc5Laz36yOry/Ory865Zeq6exATT3H9XQ2PdLc2txQ4Ia9+n1nQ2krKv3CxqeNqYn/6qOXV8OwkR3UrTcXQ7yh2PMzkDrTXa8NNxZCvp85faHS2SJI68+zl35YprfXYHCRcpjEKAwmyZ42t9Cp+K3+FOXORxxE2cpZRSdgc729CTr7828tjL57wHqMG7aZx682fXO4qDexKTTXPP/9Y1tN7Q2jZbwfhwZNOatlrS9641J7U+bSzIWGteVOjFHTVbTWnDR9Obf2XQDfdw5qkHSBw1ndad/yZpbDE1T36X9HlLSRgxYDaxv5B0oNbuEOEihVGIYNozxdf+ErT/54ArofiMM2F2xLR6ZNfuXDPhwN8WnevrzYlsu/9WZ9qxQert56QZxo576hp87w/D5sBa03SKrH2vGVM7njbn52wwCycYOKN2i6mm9f8gdfq1OBJSAEguuhRX2iD8TadJHDOb9v1rpTC+IwMpjFFDCqMIEf84f+facf7ON6uc8VMPuRIvma2UO8muNKnNR1+fumvVoq6+1lVPYpZhbL2vtl4tau8I2fJnpla1x3XuwTJzume1MT9/mx4zVuOImWb6zsptdB7dTsuWZ/GeOULdCw8TP+wi3FlDMTvbiLV1pfso3e4A4RQzhXHl0iVpQLzdOUSs0QWGZ1uB4dlW73CPD7R6JGX1Z4KEjtr1s7b88D3rn3bVkzjI79/4vZq6xLmdnul9Pa+h1ekjOv/wK+ZMY7Uxf0iFHjEGyOnrcSNV3ke///bnp/98N5mL78Bzch/u7GHUvfQLMuZHRO95pIjpwhgzu2usXLokDzhldw4R89qUq2CzO+nqMQ5n+tBwn8ztbdm2YN23ihza/67/9AX1JE5Caz3Ub2x4sKY2bYbH2+tt13zaefyAHnrsJWO2fsacN/ywHhKqpb9E7PlQ5Ypr/2l3iHCJmREj4LY7gBgQkrW/6lJv82/9ypG91p18zSCHa3BYNrF1+jsr5pXfOya4KL6rJ1GhRnl9b66oqR000esr7unxPdp1uEKPOPGCMcf5rDF31AlyhzEAmrdFSMT0XqixVBjj7A4gBhSXNusWeFv+pFEpG9zJVyY63aNC1uqhTP+ReeXLc12GJ/Wtx97uSUxjwQSvb92KmrqhY32+bm0xpTW6k7gDO/ToU88Zc+OfN+aMqSVjNGFu2RAxK6YvpcZSYZQRo7CDQrfO8bX+E5+K3+FKXNjpjJs0u0+tHto8NXfDd91xvpYcAL+D46ve7zj92kVqxmSPt/wPVXXGcL//4vMeQuNvI2H/FnNczTPmvMSXjVnjm0gZT5haNcSAI4UxSsiIUdhLe6b421/G3/6fg66EuaedCbOKlXL27D9sWjfM3vyDtsTO2rGBnsR1P7tOTZ5ieDtePl5Xl28Y71nn1HoZ3iaS9240CxtWG/NSyszpE9pInBiS70uI94rpfW9jqTDKiFFECGOsv3PtWH/nmyed8VMOuhIvmalU3IXfSLRun7bjF1WprccnNyey7Xs3O9xD0zz6pdN1Ro5hXvrup9JeR9q+debEpqeN+Zmvm1MmeIiTJjvRX3q0GXa0iaXCKCNGEWH0EMOzfYjh2d7gcI/b6E66fLJyJHW9zqTWvokVj+7JaNw74smLVVndDI/jD7X1UzOqzUnWl2muJnP/68bkttXm/Ox15sQJflx9bskQopccdgcIp1gqjDJiFJEq0/QdWORpOtCuXENftVo9Mt7ZoUBrPebwvzb41Ebfi5/o3P5FT+PslGqMk2QfeMaY1rHamD94sx4/zsQxy8bvQYhgMmKMEjJiFJEuSftPLPQ2/y7Q6nH1IIcrb1xO4xvPe8a/aA7LiU9LbZkXd6sxv2aXHjUGVMysKiNijhTGKCEjRhEtAq0efyYraexz9fPy294c9Im42vislmqV76x3Zh3zqMRqFBG3iLkQAMpjxOw6qRBbhVFGjCLqeLy10z5w+sNDLzvdeepQVvmO9oKnSck6lmm6GXVG5TWcoKChimHtJyjwV5PnaiQruY2kLAPXYJSSTQKFLXSKe6vdGcIplgpjLH0vYoBo8zcO1dqsSVEJ+VPrF+br+kvNQ47TW7bH79sZP3iva1LePn9xypujHA79rl3TNegmnVFzmvzaKoY1VzHcc5KhupbcuGbS0zpJyNWoXCJw66xoZdTX4UhLQ7nk4hRg2h0gnGKpmHTaHUCI3mg3Wo8ku9JyARTKMdbMnzW2I5+2yrnVG08c2rvDUa2TUuoO5efvr8rOOZ7qdndepBRxGTTmZtCYW0hFl8f14/Ke0YNOn2JofRXD2qoY5j9NvqOe7KQ2UjJ8uAejVEq/frMRzmxtoemBu8EwUQmJxE2fRccrz5H140fwbnqTxKs+YHfESGHYHSCcYqkwttodQIjeqO083pGcMuk9jyeTMHiRb9LghUzUR3xntmxqGRp/8EDHZIfT583NPbotL+9gZ0pq7SiHQ3e5vqkLf9wQTg4fwsnhM9nY5blbdUrTafKrT1DQdJzhHScZatYwOK6JjNQOErNNHINRKqYnWgTr/M8LJN14G/Gz5tL8k+/R/uSfSbr5E/j27UYlJNodL5LIiDFKSGEUUelE+8HUEV0UxrcolBptDp452juYdjw1m1yH9hw8HTeiunrsSICkpIYj+UP2H8vJOZYSGE12e/u1FFrTx3IgfSwHuvy6icOo09knTzK0rophLVUM951miKojO6GF1Awv8YNQKmYWlE66/qa3PzebGnDmDQW/H8+mdaR87A4bk0Ucn90BwimWCmOL3QGE6I3qjsrRWmvdnfVVk4jPvdQ/ceEl/iJ91FGzdaPrUEdTGzMPHSwedehgMQ6Hvz0n59jGvPwD7amptSMdDnNEX7I5MJ251AzJpWbIVLZ1+ZwOndBaTV71SWuiUMcJhpnVDHY1kpXcTlKmgTMPpaJqr1Tv7u3olhYSl3yIjhdXEz9/IY3fuovkj95B3PTZdseLBG12BwinWCqMMmIUUclrdmaYGJVOXCO7+xqFUiPNQdNHegfRgbd+s+vw+v3Ok8NM0zX6zJnRs8+csTbNSEpqrMzL3380J+dYclxcxySlCPn1wEQ6U0ZSmTKSyi6/rkE36syaUwypqWJY8wmGeU4ylFpy45tJT/MQP0ijsiNlopDZ3ETLz79PxvIf4cwbgmNwPsapE8QVX0Lna/+RwmiJ6ffbWCqMMmIUUavV13gyPS5nZG9em0hc1sX+woUX+ws55qjdvtF1sLVBtc1EkdDenjHy8KE5Iw8fmoPD4e/Izj6+KS//QFtaWs0Ih8Ps1fl6SoHKpCE3k4bciezu8jle3J01etBbo862Kob5q8l31pOV2EZypt+aKBT2hau1z0fTff9Lyh1fxJk3BACj6hiuYSPwtbaAjulbaz0R0vdbpdTXgBat9f8L/P7LQKPW+vdBz3kQeF1r/YJSahTwgNb6o6HM8ZZYKoxtgAZpihbRp7qj0p8el9Pn4ww3c6YO9+bQia9xq+vwhr3Ok/mGMscBmKYrsaZm1KyamlEAJCY2Hc3P31+Zk3ssKS6ufZJSJPU5QC/F4UsYyokRQzkxYjblXT6nWac2nGbImRMUNFUxrPMkBbqGQXFNZKR2kpBt4hjU14lCHc//E9+BCtr+9Ahtf3qExGuuw5E9COeI0TT/+AGSb7uzL4ePJSEbMSqlngNGAV6l1FvTfkcBhlLqcq31x5T153o18FOl1AeBtYTxPqfSWofr2P1u5dIlbWDfP24hemtQwojdl+XffO4ZOH1Q5ajbucF1sKletc7gHMVPOfyd2dlVu/Pz97empdUMdzjMUeHIEk4GDn8tudWBiUKtJxjmPU2+o46cxFZSM7zEDUapNLtzxoglpy+b9lyoDqaUugvIBV4IPPQRYKvW+tHA128B3g98Dfg58Dngr8BmYKTWemmoskBsjRjBGt5LYRRRp7azaqzW2qeUCnn3eIGZPbnAm40HX9M2V+VrFc6qwX5lTgh+jjZdCbU1I2fW1owEICGx+Xh+3oEjObmVCfHx7ZOUivz995yYrsFUDx1M9dDpbOnyOR06seU0eWesFYWGd5ygwDzDYFcjmSkdJGUFJgpJB/+FNYThmAnAW321b0/WUtbEra8Bu4EpwGzgUSAN+AewMtRBYq0wjIEN7AAAIABJREFUtgKD7Q4hRE+ZGPF+7dvjVnFh21w4Hnd6sX/cpcX+cZxSDXvK3QfqalXLdBTvafLv7EgbduTIzGFHjsxEKcOTnV21JS9/f0t6+pkCh8McE66M4ZZIR+oojqSO4kiXXzdRZoPOqg6sKNRSxXDPKYYoa6JQWrqX+BytHH2/5h396sNwzGuBmYHPC4D1gc/nAE9gXUq9AdgK3An8CNhGGBYbiMXCKERUavSeqc1NKLjwE0MgX2dO/KB3Dl78Ldtdla/vdlbl+JVR1NVztXbG19aOmFFba3V+JCS0VOXlHTicO6gyPj6+bZLqorBGKwfakU3d4GzqBk9iV5fP8ei4jjMMfnuikLWObb6rnqykdpIz/bjyUCrWVwMIyYgxcO/wrRlNK4Iund4V+NWBdT+xAigCvgP8IvB8F/AI1uXUkIq1wthsdwAheut0+2FnfxXGt8ThSp3tH3vJbP9YqlXj3nL3gZozqnkqinPei+vsTC2orJxRUFk5A6UMb1bWia35+fub09LPDHU6jbH9md8O8XgTh3F85DCOjyxmXZfPadZpdacYUnOCguYqhneeZGhgolB6qoeEnMBEoWje7DdUl1I/DtxIYOKkUurGwOMq8NhS4Jfwnh+0K/CcLwMupVS21rouRJlibvLN37F+yEJEnYy4QYevHvrJ0Xbn8OFv2+E6tmWX81iWTxk9mhAUH996Mi//wKFBuZXu+ITWSUqRGq6c0cyP01fLoOqTDKmzRp3DfKcY4qgnO7GVlHRfZE8Uaj192bSQ/rkqpb4O5GitvxH4/VPAy1rrVYHf52BdOv0y8Gesy67OwH35LwFZWut7Q5Un1kaMJ+wOIERvNXrPjNJaNyub3xDduJJn+kdfMtM/mhrVfGC9+8CpatU4FUX6hV7r8aQMOVo5fcjRyukoZfoys05sz8870JCecXqI02mM74/8vdXcbHBgv4ex4+JJT3+n66Oh3k9qmhOXK3SdYC4Mdx6nCvI4VTDjHFcC23RSc7W1jm2jtaJQgT7DYLc1USgx28Q5GKXseA8/GaoDKWvrtL9gTaz5ZtCXbgV+pJT6/+3deXhc1X3/8feZGa2WLMmytdiDLSPjHa+xMcvYYJdQQAkhBJSGJECztlmaZOjyS9rGT9Ok00XPL2la0vySPNAmDZkkJF0mCWQBHLMZgzHGxmBpLFuy9n0bjWY7vz9mHAYjy1pmdGb5vp5Hjyzp6t4Pi+cz995zz3lIa30v0WUF87XWA0qpU8Bj0V9XAINER6kmTKadMf4p8A+mcwgxW7ct/+RL+dYFW03nuFCI8Phxa8uLx2wtJQEVunI2+8jLG+uoqmpsWlLRnJOfP7pOTaNo58vISJgvfL6Tq3YV8sQTo1y/p4hnnvXR0FDNwYNjvP3tqXfiG0FF+inv6oiedY6cY3mgg6Wqj8V5IywsCZBboZVlURIO/UTnDVv2JmpnSqkarfWZi/ysUmvdlahjTZecMQqRQvonOoaXFqbebTob1oIt4ZXXbQmvpFeNeA/ZGs91WAY2oSib7j4mJhZUnz27pfrs2S1AJFS2qP3l6upTA6WlndVWa3jNJXeQRKdPB/j4H5Wzfn0+oyMRHn10hPe+t5TXX58gPz815wyxoC2L6a1eTG/1lRybdJsJneeLzmO7rD82j224kyrr+XlsYwOFZrrg9bm5p3/DxUox9rN5L0WQYhQipbT7vPmpWIzxFuvi2luD22rDRCZOWFufftl2tmhCBTfPbC8W20C/ffNAf3SwUW6ur6uqqrGxorLZmp8/sn6+zyY3b44OIj12bJzXXptg8+Z8QmHNiy+Mc/f703fxkDwmCpdzduVyzl50woZBXdrbSXVPdMHry/wdLKOHirxhSoovsuB1xr/OZloxJvSdjBDzrcPnndNqGPPJiiVvU3jFtZvCK+hXo82HbI0tbZb+DShm/JxfIFBY2dKyubKlZTMQCZeVdRyrrj7VX1rWWWmxhNYqlfypHrXWPPnEGEXFFjZvLuDxJ0a5+upC/uqvurj7faVs2ZqZT2CUMri4lMHFUy143aOXdLVj7z/HZaNjLHgNtsxzyvmVafcYc4FxIJ2HQYssd2fNn3ZYlKXadI7ZCBMJnLSee/Go7Uy+n+AWElBoObm+nqqqplMVFc2qoGB4nZrB5dvZePDBfmpqcqmstNHREWJgIERHe4hPfVqe64+5dd9e789Nh0imjDpjdLo9gYb6unaisyYIkZZ8oeGWopzStCxGK5bcjeHlV28ML2dQjZ09ZGs802rpW49iyWz3GQwULmlt2bSktWUTEAmXlna+Ur30VF9paUeF1Rpal4izyR88PMiicitvf3sxo6MRioostLUFsdtzGB0NE8mc84dEmHzaoAySUcUYcxopRpHGevyt/qKc9L2vdV6pXrDipuCWFREiwdes7c+9ZGvOGSewFTWXKzoW6+Dg0isHB6NLQuXkjPdWVnlfr6w4TUHh0DqlmNUozFvrivnS33Tzi5+PULMyl3Xr8nn9NT8rVuTy1f/by/s/kNST1HSiyYJizKhLqQAN9XUPAfeYziHEbC0rvOLodZXvzsibOMPKd+6QrcnbYulZoxVVid27jpSUdp6srj7VU1bWscRqDa5TcyphMYmOfXu9S02HSLZMPGPM+HczIrN1j5+9XGsdUek9ZdikFupC+43BTfYIOtxo7Xj+iO20GmNiG4o5raMYpSxDg9UbhgajV6Fzcvx9FZXe1yorvRQWDq1RsxgUJN4iK15fM7EYT5sOIMRcBHVgYUSHvVZlS9tVLC7FgrKuCS/duSa8lBHGO57PaTp1xtJ9hVYk7GwkGMwvbzu34dq2cxsAHSkp6Xq1uvpUT9mi9kVWa3CDnE3OSla8vmZiMZ4wHUCIuRoJ9neW5lVkbDHGK6agel/wymqNjjRZOg+/mHNaj+Lfhkrk65OyDA1VrR8ail69tdn8A5WVp09WVnojhQsG16g5DA7KMl7TAeZDJhbjcSAIyGKjIm11+s+ES/MqTMeYVwpluSJSveOKiWpG8Xcezml67bSle5VWOuGD6UKh/LK2tvXXtLWtB7ReuLD7terqxs6yRW2LbLbABpWQS7sZafIpdjJMxg2+AWior3uZ6ErPQqSlxXn2k/uW3j3p+ojZRKP1aUv3kRds3uCIGt+OSv4bXpttYtBmO9606oq2idLSoSuUIrveoUytdt9eb8ZfTs3EM0aIrvAsxSjSVt9E+yqttV/NfB7LjKJQqjZSub02UImPiZ7DNu8Jr7VzZUTpOc8Q5Pf7eeSRR4hEIuTm5lJTU8OxY8e45557Sk+etL3N738noHXxwp7Xq6sbOxYtOlcWO5vM1NfNSxlGBt+ktaPIIxsijWkiOUE98Xquyt9oOkuqKCRvyZ7Q+ut3h9bps5aelw7bvP4h5duOInc2+3vllVfYtWsXtbW1eDwennvuOa699lra29vJyTl/YqrUyHDFmpHhijUAVmtgqKLy9KtVld7wgqKBVUrpBD9yktKO7dvrzbxLjJPI1GJ8yXQAIeZqcKK7v6JguekYKUehVE2kYmtNoIJxAn0v2rzHT1k7lkeUvuhE2ZPZsWPH7/7s8/koKysjEong9XrZvXv3pL8TDueWdLSvvbqjfS0ARcW9p6qrT7WXl58rtdkmNmb42eTLpgPMl0z9j3jUdAAh5qpj3GuTYpxaAbnl14XW7bkutI4WS+/Lz9uaxgbV2DYU074E3drait/vZ9u2bRw9epQ1a9bw8MMP43A4WLly6q4dHVm8unFk8epGwGoNDFdUNL9aWeUNFhX1r1JKp+W0flPImtfVjBx8A9BQX3camNE7SCFSycKc8rM32z+cNqttpAo/gYEjtuZjr1vbl4VVZMo1vMbHx/nud7/LXXfdRWlpKefOnWNgYIDR0VEGBga45ZZbZp2jqKivsbr6VFt5eWuJLWdio5qHgUNJtmPfXu8LpkPMh0w9Y4TouxspRpG2hoN9K7TWg0qp9J84dR7lk1t2TWjNnmtCazhn6XvleVvTcL8a3YbiTetGhcNhfvSjH7Fv3z5KS6P/ivv6+igvL8fv9zPXk4bR0fIrGhuvvqKx8Wqs1uDIkiXNL1VWef3FxX21Sullc9r5/PNj+FKqUmo/8KTW+slkHyuTi/El4HbTIYSYC394zFtgK9puOke6skfKr7QHypkgOPSSrfnwSWtbVVhFVgMcOXKEjo4ODh48yMGDB9myZQvFxcUsWbIEj8fDnj17EpYjHM4p7uxcvbOzczUACxb0e6uXnjpXXt66MCfHv0HNcgDRPDq6b683aDrEfMnkS6nvAP7HdA4h5uLaitsP2BesTtwrtKDd0n/ieVvTQK8a2Ypigek8FktwbMmSsyeqqhv9RUV9l1ssiZ/QIAG+tm+v9zOJ2FHsEaSHiK6CNAjcpbX2XbBNAfAjYCHQB9wJ/CXRiVscse//PjAK/BhYADRpre+L/f6TwGFgk9b6ptjZ5lVAIdADvFdrHbpYxkyeK/CI6QBCzFWbrzEzl403aGlk0YZ3BXZe98GJPZFNoRW/tWnr5EvXz5NIJGdBV9eqnS8fvXn300+9337kxbrT7e2rDwQC+Ue0ZsJktjjPJ3BfHwVe1lpfBzwCTPZI0nogorXeDTwIFMW+vyr2vZ8Ae4Fq4OvA7wE1SqnK2Ha7gGe11jfF7fOg1noP0AXcNlXAjL2U6nR72hrq67xAVsw3KTJT53iz3CdPklxsxTtDq3bvDK2iUw2+diinsbtHDW9FUWwy19hY2eXepqsu9zZdhcUS8i1efPZwVXXjeHFxb43Fok0NUz6UwH2tJVqIED1znMwR4LhS6pdAI/Bo7Pv/EfvcAuQSnf7zw8B9wCL43X3k41rrn1ywzxdjn48BNVMFzNhijPk1UowijfnDY0siOtxmUdZ0G6yRVqp06drbAjvWBgmNHbOdPXjc2rooqMIbTOeKRGyF3d21O7q7oy9jhYWDzdXVp1rLF7cU5uaOb1QzeCxlDvr27fUmcvLw14AdwG+AzwPdwLcu2GYz8LTW+vNKqe8TvXwKMHbBdh8iein1h8CBuO+PTnLcncBjwFbgF1MFzPRi/A3wMdMhhJiLsdBQS3HOIinGeZCDbcH2UK1je6iWbjX0+qGcxq4uNbQZRYnpbAA+X+lKr3fnSq93JxZLaLx8ccsL1VWNY8ULe2sslkiyHu15JsH7+xbw77H7gH3A3ZNscwb4e6XUF4iOiH2BN8ox3q+AB4CPx75eFvvdyeyIHbMT8EwVMGMH3wA01NctJvpuRJnOIsRsva38pgO1C7fIABxDgoR9x60tR16xtZQEVOhK03kupqBgqKW6+lTz4iVnz59NJur+9Kf37fV+PUH7MmKmj3pkdDECNNTXvQRsMZ1DiNmqLqg9trvqPTIpfgroVcNNh2yNbR2WwU0oykznuRhlCfkXl7cer6puHFu4sGe5xRKZy73qtfv2el9PWLgLxM7i4g1praccHJNsmX4pFaKXU6UYRdrq9rfUaq3DSilZI9CwxXrhqluD21eFCPtPWM89fcx2pmhChTabznUhHbHl9/SsfFtPT7QPCwqGWqqqms4sWXImPzfPt1EpCqe5q9ZkliKA1vr6ZO5/NrKlGJ2mQwgxW2EdXBDWoUabyrnCdBYRZcOavzm84trN4RX0q9HmQ7bGljZL/0YU5aazTWZ8vGR5c/P25c3N21EqPFFe3vpiVXXjaElJt91iiUw1QPFX8xYyhWRDMf6W6JDedJ+nUGSx4WBf16K8KinGFLRIF628Obh1ZZhI4KT13DNHbWcK/AS3oFJzbIPW1rze3prtvb01AOTnD5+rqm46vWTJmfy8vLH1Sv3umUHI0mLM+HuMAA31dQeB60znEGK2rixzHFxfes1ko/JEChpQY2eftzU2t1r6NqBYYjrPdCkVDixa1HaiuvrU8MKS7qVWa/jafXu9PaZzzbdsOGOE6OVUKUaRttp8TZXrS68xHUNMU5lesOKm4JYVESLB16ztz71ka84ZJ7AtVc8iz9PamtvXt3xrX99ygBf279+fdaUI2VOMvwa+aDqEELM1MNFZq7X2KaWmO2hCpAALlpz1Yfuu9WE7Q8rX+ryt0dti6V2nFZWX/m3jpnzWL5NlSzEeAgYgdYdXCzEVjbYGIv6mPGuBPLaRpkp04WU3BjdfFiESOmXtOHTE1mzxMbEdlbJzVv/MdABTUvU/SEI53Z4g8N+mcwgxFwOBrgHTGcTcWbDY1oaXXfW+iet23BW4pmNluOJJpekwnesCnbwxt2jWyZYzRgA3cK/pEELMVofPm1tVUGM6hkighbpg2b7glcsi6HCTtePwi7ZmPYZ/OwrTz6x69u/fn/kjMy8im4rx10A/0RnYhUg77T7vZVvL95mOIZLAgrKuDi/dsTq8lFH8HYdzml4/belepZWxtRl/ZOi4KSErLqUCON2eEPBT0zmEmK3R0IBda91rOodIriLyq28Ibrz+DyduWHp9YMOLxZH859BcdFHdJOgHHp/H46WcbDpjhOjl1A+ZDiHEbI2HR5oLbQsXm84hkk+hLKsiVdtXBaoYY6L7hRzvq15L58qI0slaReO8/9q/f/98FnHKybZifBzoBeSFRaSlXn+bb3nRQtMxxDxbQF7FnuD6it2s02cs3S8dtnn9w2p8O4rcJBzuh0nYZ1rJmkupAE63JwxcuKqzEGmjzddYdOmtRKZSKLUyUrn1rsA1V9894RheE1p6wKJVcwIP0UN0QpSsllXFGOM2HUCI2eocb77cdAaRGgrIXewIrdvzhxN7V94Y2PRyaaTwaTQTc9ztj5JxGVUpda9S6t5E7zdZsu1SKsABoAvSYuYJId4kEPGXRXS4xaKsy01nEaljRWTJ5hWBJYwT6D9ia37ulLXdHlZTrppxMd9PeLg0lHVnjLHLqY+YziHEbI0GB86ZziBSUwG5i64Nrdlz38QNtTcFNr9SFlnwFJrxaf76GeCZRORQSu1XSv1CKXVAKfVjoidhm5VSjyulXlVKbYydRT4R+3hMKVWSiGMnQtYVY8wPTAdIJ76JAKc6exibCFxyW38wxHggOA+psleXvyWrRwyK6bkssvjKOwK7rvvAxO7AxtBlB6za0niJX3kwwQ/1H9Ra7yF6ha4E2AHcBLiAd8a2adRa30D0OfMPJ/DYc5KVxeh0ew4CJ03nSAe+QJDvPHWYlv5BvvHkc4z6J/hbz2944IlneeCJZ+kYHOZ4Wyf/+OgBBn3jvNbRTY7V9KQdma3N1yiTVIhpyyOnZFdo9Z77Jm644ubA1hPlkeKDaMYu2CwCPJjgQ5+fUu5Y7PPDWusg0AK/G00bv01Ngo8/a9l4j/G8bwD/bDpEqusYHOadW9azoryM8UCQ55vPseWypdRtXve7bZ7xnuXWTWs50ztAWEewWbPy/da86fG3rtJah5RS2fz3V8zCssiiDbcHdhIgNHzUdua3r1pbK0Iqshb41f79+1sTfLidwGPAVqInIheW8fltvhnbpinBx5+1bP6L9e/AVwAZ/j6F2opyALw9fbT0D7HJXsXJjm68PX1UlxRzx/YrUUoRDEdo7u3nhrWzud8vZiKiw/khHTyZo3LXXXprId4qF9vCnaFVu3eGVtGhBk6esJ371yQcZodS6kmiE5KPXmSby5RSTwB+4K4kZJgVpXXWzhNLQ33dN4CPm86R6rTW/PTICYbG/exZczmLiwpZWJDPw4eOssleTX6ujUdfeZ0rl1Vxpm+Qjcsq2bZimenYGW1f9fsPLs5f5jCdQ2SETmC53eVI2OAApdR+4Emt9ZNTbHMvgNb6oUQdN1Gy/ZrXA6YDpAOlFO/evpHqkmKGx/0sLMgHwF5WQs/oGLVLyrl711ZKCgsoLyqkqVum80y2zvHTKb0SvEgr30lkKQJorfdPVYqxbR5KxVKELC9Gp9vzCnDQdI5U9vhJLy+ciT4dMB4M8siLr9A+OEwkojne3sXS0uj0ZN6ePlYuLsOiFCCv2cnW5vNWm84gMkIE+H+mQ6SabL7HeN6/AnJJ6iJ21S7nu88e4dDpVqpKivjjG67m+4eOooENSytYXbmYiNbkWq0U5+dxpm+AjUtl7oRkGwx0rdRajyilik1nEWnt53aXo8V0iFST1fcYARrq63KIDh+uMp1FiJl41/JPHc2zFm4xnUOktZvtLsejpkOkmqy+lArgdHuCwLdM5xBipvomOoZMZxBp7ZiU4uSyvhhjvgnzuhCoEHPW4fPmmc4g0trfmw6QqqQYAafb0wb8l+kcQsxE+7g32QvWisx1Gllp6KKkGN/wd6YDCDETvtBwtdaRLtM5RFr6J7vLETYdIlVJMcY43Z4jwP+aziHETPhCI2dNZxBpp4vEz4uaUaQY32y/6QBCzESPv3W6SwoJcd5X7S6H33SIVCbFGEfOGkW6afM1LTSdIdnah+VqcQINE11AQUxBivGt9psOIMR0dfnPXK7T8GHknrF+3v2fn3zT9+778V9woiu6ZKDrwDe52+1Ea80zLS+ZiJipHrC7HPKYzyVIMV5AzhpFOglGJkoihM+YzjETg/4RPvuzrzAefOMq8E9P/JIVpUvZUHkFAH2+Qa6sWs3xrlMsWygzKSWIH/iq6RDpQIpxcvtNBxBiukaCA+2mM8yEVVl44Lb9FOUuAGBgfJgvPfEAJfnFPHP2CBBd0SUUCXOo9WV2XSaT+yTIN+0uh1yXngYpxknIWaNIJ13jZ9Jq2H1x3gIW5r2xDOq3D/+QW9dcz91b3smPjz/GLxufYs2Sy2kb6sKiLNzx/U/R2HvGXODMMAT8rekQ6UKK8eL2mw4gxHS0+RoXm84wFye6G7ln2+1UFJXzjrU38GzLUT6y4y7evfHtFOTkc/Pq3fzG+6zpmOnOZXc5ZD24aZJivAg5axTpos/ftkprPWE6x2zVlC6jZTB6Nfjlztexl0TvKQ77R1mQW0CuNQdN2o0vSiWtyL3FGZFinNoXia5XJkTKihDJDelAk+kcs/Xxq97HQ0d+wu3f+2Oeb32Z+itv5XR/K+srVrGleh0PvfiI3Gecm7+W5xZnJuuXnbqUhvq6bwEfNp1DiKnsrX7fb5fkX7bbdA6Rco4BW+0uh7zBnwE5Y7y0zwODpkMIMZUO32mr6QwiJf2ZlOLMSTFegtPt6QH+2nQOIabS7mtaajqDSDm/srscj5kOkY6kGKfnAeAV0yGEuJihYG+N1lpmNBHnaeDPTIdIV1KM0+B0e8LAp0znEGIKaiLi85oOIVLGd+0ux1HTIdKVFOM0Od2eA8jCniKF9fnbR0xnECmhH7jfdIh0JsU4M/cDPtMhhJhMm6+pwHQGkRLut7scPaZDpDMpxhlwuj3ngK+YziHEZDrGT9eYziCMe8LucsgixHMkxThz/wTIvRyRcvzh0YqIjqTVhOIioSaAj5sOkQmkGGfI6fZMAJ8xnUOIyYyFhlpMZxDGfNnucpwyHSITSDHOgtPt8QAPm84hxIV6/C1pO2eqmJNXAZfpEJlCinH2PgG0mQ4hRLw2X1Op6Qxi3mngo3aXI2g6SKaQYpwlp9szAHzIdA4h4nWPn63VWssUYNnlW3aX42nTITKJFOMcON2ex4B/M51DiPNCOlgU0aHTpnOIedMO/LnpEJlGinHu7gfSdskfkXmGg/2dpjOIeaGBD9pdDlnkIMGkGOfI6faMAfcAYdNZhADoHG+WS6nZ4Z/sLsdvTIfIRFKMCeB0e54B/tF0DiEA2nyNlaYziKR7AfiC6RCZSooxcb5IdFFQIYzqn+io1VqPm84hkmYUeJ+MQk0eKcYEcbo9AeADQMB0FpHdNNoWjEzIfe/M9Wm7y9FoOkQmk2JMIKfbcwxZ1FikgIFAV7/pDCIp3DIXavJJMSbePwA/Nx1CZLcOnzfHdAaRcGeBj5kOkQ2kGBPM6fZoopdUzxiOIrJY+7jXbjqDSKgwcLfd5RgyHSQbSDEmgdPt6QfuAPyms4jsNBLsX661lsupmeOvZHab+SPFmCROt+cI8CnTOUT2Gg+Pygw4mcFtdzn+znSIbCLFmEROt+fbwHdM5xDZqW+ibcx0BjFnLwF/aDpEtpFiTL4/Bp41HUJknzZf0wLTGcScdAO32V0On+kg2UaKMclizze+G1miSsyzTl/zStMZxKwFgHfbXY5W00GykRTjPHC6PZ3A7chgHDGPJiK+8ogOywtrevqEDLYxR4pxnjjdnsPAR0znENllNDh4znQGMWP/Ync5vm06RDaTYpxHTrfne8BXTOcQ2aPb3yJTFKaXx4HPmg6R7aQY55nT7fkCIO8Gxbxo8zUuMp1BTFsTcKfd5QiZDpLtpBjN+DjwU9MhRObr8bfWaq3lhTb1tQE32l0OmZQhBWR0MSqlapRS18d9fa9S6l5ziaKcbk8Y+APgScNRRIYL61BhWAe9pnOIKfUDN9ldjjOmg4iojC5GoAa43nCGSTndngngNuCo6Swisw0FertNZxAXNQrcYnc5TpgOIt6gtNamM8yYUmo/cBVQCPQAjwHlWmtX7IywChgH7gNKiU7ofSdwK7A59lEF3KW1Pq6U+jqwBRgEPki0sN6yXTL+WRrq6yqBp4BVydi/EBtLr3tqQ9m115nOId4iANxqdzl+bTqIeLN0PmM8qLXeA3QR/R/sXbHvvwf4ntb6a8BngIe01tdrrXtiP98B3AS4gHcqpeqAfK21A3gE+PPJtkvWP4TT7ekC3g50JOsYIru1+ZoqTWcQbxEhulqGlGIKSudifDH2+RiwGPAqpfYAFq31VM9uPay1DgItQC6wHjgU+9lzwLqLbJc0TrenGfh9QJaUEQk3EOis1VqPms4h3uRjdpfjx6ZDiMmlczHujH3eSnSY838ADwIPx20zTvRyK0opFfvehRMrnwB2xf68K/b1ZNslldPtOQa8A5kdRySeJRDxywCc1PEX8gB/akvnYtyhlHqS6D1ED/BrIAf4Sdw2LwGrnl9iAAAKYUlEQVRrlFIHgfrJdqK1/hkwrpR6iugaiv+YzNBTcbo9B4leEh43lUFkpv6JjkHTGQQAX7G7HH9vOoSYWjoPvnlSa/1k7OtFRMvxl1rr/eaSJUZDfd0e4H+BYtNZRGa4YuG257aV37jr0luKJPpLu8vxZdMhxKWlZTFmg4b6up3Ao0CZ6Swi/S2wlbbVXfaxZaZzZLHP2F2Or5kOIaYnnS+lZjSn2/M80Wcw5Rk0MWdjocFlWkd6Lr2lSLAI8FEpxfQixZjCYgNydgOyQoKYM194pNl0hiwTAj5odzm+ZTqImBkpxhTndHteBxzAadNZRHrr9Z+TQV3zJwDcZXc5/tN0EDFzUoxpwOn2nCFajicNRxFprM3XJIO55sc4cJvd5ZCFAtKUFGOacLo97cAeoo+gCDFjXeNnLtcy2i7ZRoCb7S7Ho6aDiNmTYkwjTrenB9gLHDCdRaSfQMRfGiF81nSODHYOcNhdDvn7meakGNOM0+0ZBG4E5Ia+mLHR4GC76QwZ6iiwy+5yvGw6iJg7KcY05HR7gk6356PAnwBh03lE+ugaPyOLFifez4ieKbaZDiISQ4oxjTndnn8GbiG6XJYQl9Tmayw3nSHD/DPRgTYySXsGkZlvMkBDfd0a4H+A1aaziNRmwTrxnhqnUkoldcWYLBAEPiHPKGYmOWPMALFnHa8CfmU6i0htEcJ5IR1oMp0jzfUCvyelmLmkGDNEbFDOzcDXTWcRqW0w0NNrOkMaewXYYXc5fms6iEgeKcYM4nR7wk6359PAx4he6hHiLTp9p62mM6SpbwNX2V2OM6aDiOSSe4wZqqG+7mrg+0CN4SgixZTmVpy+adl9l5vOkUZGgI/ZXY6HL7mlyAhyxpihnG7Ps8AWQP4yizcZDHSv1FoPm86RJl4CtkkpZhcpxgzmdHuGnG7P+4B7iL7rFQJATUR8XtMh0sC/AFfbXQ4ZrJRlpBizgNPt+Q9gK/C86SwiNfRPdMgZ48UNAnfYXY5P2V2OCdNhxPyTYswSTrfHC1wLfIXo4qkii7X5mvJNZ0hRh4CtdpfjJ6aDCHNk8E0Waqiv2wN8D7CbziLMKLAWdb1z+ScqTedIIQGibxq/Ync55mVEt1JqC7BFa/3QfBxPTJ8UY5ZqqK8rIzoR+R2mswgz7qz5006LslSZzpECngc+ZHc5jpsOIlKDXErNUk63Z8Dp9rwHuBvoMp1HzD9faDjbl6AaB+4HrjFRikqp65VS+5VSS5VSTymlDiqlvhz72bdiZ5Qopb6plNox2XYiOaQYs5zT7fk+sBb4N+TeY1bp8bf6TWcw6ACwye5yNNhdDtMr1CwD/oLozFXviH3vx7GvAdZqrQ9fZDuRBFKMAqfbM+h0e/4IuIbounIiC7T5GktMZzBgGPg4cEMKPYYRIlp43waKY9/7DXCVUmod8MIU24kkkGIUv+N0ew4BbwM+B8gyOhmue/zs5VrrbLpK8HNgg93l+Kbd5UilwRWfA/4O+DCgAbTWIaAH+AOiZ4+TbieSQwbfiEk11NfZga8B7zadRSTPe1Y4vVaLrdZ0jiRrAu63uxz/bTpIPKXUzURnpzoN/CXRIqwAbtJatymlbgG+CqzRWmulVP1k25lJn9mkGMWUGurrbiU6A0iN4SgiCW5aet/TpXkV15rOkSSDwN8A/zJfj2BMh1KqhuhUjWHgI1rrk0YDibeQYhSX1FBfVwj8H+CzwALDcUQCbS67/rdrS6/abTpHgoWAbwJftLscfabDiPQjxSimraG+rhL4AtFlrWQF+AywOM/+2r6ld681nSOBfgE47S6HnIWJWZNiFDPWUF+3AtgPfACQtf3SmMISvLPm/rBSKt2niHsV+Jzd5XjMdBCR/qQYxaw11NetA76EzJ6T1m5f8SfHcy35G03nmKXTREdqPpgCzyOKDCHFKOasob7ubUTnmbzRdBYxczdU/cFvKwqWp9t9xlPAl4Hv212OkOkwIrNIMYqEaaivu57ou/ddhqOIGVhbsvOZzYtuuMZ0jmk6Afwt8EO7y5FNz2CKeSTFKBKuob5uH9ERrLcAynAccQkLc8rP3mz/8ArTOS7hKNFC/EmKPZwvMpAUo0iahvq61cCfAPcChWbTiKncVfNng0qpUtM5JnEY+JLd5fhf00FE9pBiFEkXW+Lqo8AnkTUgU9I7L/vECwW2oreZzhETIDoN2gN2l+Np02FE9pFiFPOmob7OBtwJfAbYaTiOiHNNxbsOXLZgzR7DMc4SfTD/O3aXo9twFpHFpBiFEQ31ddcQvQ95O/IspHE1RRsPX7Xk1h0GDq2Bx4AHgJ/JgBqRCqQYhVEN9XVVwPuBe4B0fZYu7eVbF/TctvyTS+bxkP3Ag8A37C6Hdx6PK8QlSTGKlNFQX7edaEG+Dyg3HCfr3Flzf5tFWZcl8RA+wAP8APi53eWYSOKxhJg1KUaRchrq63KAm4iuRXcbMnH5vLjF/pFni3MWXZ3g3QaIXir9AfA/dpdD1vkUKU+KUaS02Moe7wDeC9wM5JlNlLneVn7TgdqFWxIxACcMPE60DH9idzkGE7BPIeaNFKNIGw31dcXAPuD3iZbkcrOJMkt1Qe2x3VXv2TTLXx8HDhC9VPojGVUq0pkUo0hbsUnMbyZalLuRs8k5saqcsTtWfDZfKTXdUcKvEL1M+kvgoN3l8CcvnRDzR4pRZITYJdcbeKMoa80mSk93rPjcKZslZ/VFftwL/JpYGdpdjvb5SybE/JFiFBmpob7uCuB6ohMJ7CD6KIg8L3kJNy794MFFedWO2JddwLOxj8eBI/KcocgGUowiK8TOKLcRLcmdsY/LjYZKLT7g6NqSnY9tXnRDE/Cs3eVoNh1KCBOkGEXWaqivK+eNotwBrAdWkNlnlhroAJqIrljxYuzjNafbIwv9CoEUoxBvEpvPtYboPcpVF3y+HMg3Fm76IkAr0fI7/+E9/9np9vgMZhMi5UkxCjFNDfV1ClhGtCRrgUqgDFgU+7jwz4mcmCBAdBq1fmAg7s/nP/qAFqLl1+x0ewIJPLYQWUWKUYgkaaivy+WNsiwDbJNsdrG/gBoYIVZ8TrdnLCkhhRBvIcUohBBCxLGYDiCEEEKkEilGIYQQIo4UoxBCCBFHilEIIYSII8UohBBCxJFiFEIIIeJIMQohhBBxpBiFEEKIOFKMQgghRBwpRiGEECKOFKMQQggRR4pRCCGEiCPFKIQQQsSRYhRCCCHiSDEKIYQQcaQYhRBCiDhSjEIIIUQcKUYhhBAijhSjEEIIEUeKUQghhIgjxSiEEELEkWIUQggh4kgxCiGEEHGkGIUQQog4UoxCCCFEHClGIYQQIo4UoxBCCBFHilEIIYSII8UohBBCxJFiFEIIIeJIMQohhBBxpBiFEEKIOFKMQgghRBwpRiGEECKOFKMQQggRR4pRCCGEiCPFKIQQQsSRYhRCCCHiSDEKIYQQcaQYhRBCiDhSjEIIIUQcKUYhhBAijhSjEEIIEUeKUQghhIgjxSiEEELEkWIUQggh4kgxCiGEEHGkGIUQQog4UoxCCCFEnP8PdIN9Skt06SMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['pl_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的编程语言')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 大数据工具 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_e3676406_5cc0_11ea_9d85_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >bd_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow0_col0\" class=\"data row0 col0\" >2</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow0_col1\" class=\"data row0 col1\" >hive</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow0_col2\" class=\"data row0 col2\" >33637</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow0_col3\" class=\"data row0 col3\" >33214</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow0_col4\" class=\"data row0 col4\" >15521</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow0_col5\" class=\"data row0 col5\" >65000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow0_col6\" class=\"data row0 col6\" >85</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow0_col7\" class=\"data row0 col7\" >10.48%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow1_col0\" class=\"data row1 col0\" >3</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow1_col1\" class=\"data row1 col1\" >storm</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow1_col2\" class=\"data row1 col2\" >33537</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow1_col3\" class=\"data row1 col3\" >27500</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow1_col4\" class=\"data row1 col4\" >12554</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow1_col5\" class=\"data row1 col5\" >65000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow1_col6\" class=\"data row1 col6\" >45</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow1_col7\" class=\"data row1 col7\" >5.55%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow2_col0\" class=\"data row2 col0\" >4</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow2_col1\" class=\"data row2 col1\" >hbase</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow2_col2\" class=\"data row2 col2\" >32500</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow2_col3\" class=\"data row2 col3\" >32200</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow2_col4\" class=\"data row2 col4\" >14000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow2_col5\" class=\"data row2 col5\" >60000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow2_col6\" class=\"data row2 col6\" >12</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow2_col7\" class=\"data row2 col7\" >1.48%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow3_col0\" class=\"data row3 col0\" >6</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow3_col1\" class=\"data row3 col1\" >kafka</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow3_col2\" class=\"data row3 col2\" >29306</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow3_col3\" class=\"data row3 col3\" >30833</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow3_col4\" class=\"data row3 col4\" >12500</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow3_col5\" class=\"data row3 col5\" >60000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow3_col6\" class=\"data row3 col6\" >18</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow3_col7\" class=\"data row3 col7\" >2.22%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow4_col0\" class=\"data row4 col0\" >7</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow4_col1\" class=\"data row4 col1\" >hadoop</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow4_col2\" class=\"data row4 col2\" >28007</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow4_col3\" class=\"data row4 col3\" >27500</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow4_col4\" class=\"data row4 col4\" >7000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow4_col5\" class=\"data row4 col5\" >60000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow4_col6\" class=\"data row4 col6\" >293</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow4_col7\" class=\"data row4 col7\" >36.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow5_col0\" class=\"data row5 col0\" >9</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow5_col1\" class=\"data row5 col1\" >spark</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow5_col2\" class=\"data row5 col2\" >27001</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow5_col3\" class=\"data row5 col3\" >27500</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow5_col4\" class=\"data row5 col4\" >7000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow5_col5\" class=\"data row5 col5\" >55000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow5_col6\" class=\"data row5 col6\" >316</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow5_col7\" class=\"data row5 col7\" >38.96%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow6_col0\" class=\"data row6 col0\" >10</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow6_col1\" class=\"data row6 col1\" >flink</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow6_col2\" class=\"data row6 col2\" >22315</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow6_col3\" class=\"data row6 col3\" >23333</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow6_col4\" class=\"data row6 col4\" >7000</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow6_col5\" class=\"data row6 col5\" >37500</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow6_col6\" class=\"data row6 col6\" >27</td>\n",
       "                        <td id=\"T_e3676406_5cc0_11ea_9d85_701ce71031efrow6_col7\" class=\"data row6 col7\" >3.33%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19231c362c8>"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'bd_')\n",
    "data_pl=data_pl[data_pl['head_count']>10]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].values.sum()\n",
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['bd_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的大数据工具')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习工具 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >ml_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow0_col1\" class=\"data row0 col1\" >mxnet</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow0_col2\" class=\"data row0 col2\" >28295</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow0_col4\" class=\"data row0 col4\" >9014</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow0_col5\" class=\"data row0 col5\" >60000</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow0_col6\" class=\"data row0 col6\" >301</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow0_col7\" class=\"data row0 col7\" >9.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow1_col1\" class=\"data row1 col1\" >keras</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow1_col2\" class=\"data row1 col2\" >26092</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow1_col4\" class=\"data row1 col4\" >9339</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow1_col5\" class=\"data row1 col5\" >58417</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow1_col6\" class=\"data row1 col6\" >239</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow1_col7\" class=\"data row1 col7\" >7.48%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow2_col1\" class=\"data row2 col1\" >pytorch</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow2_col2\" class=\"data row2 col2\" >25679</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow2_col3\" class=\"data row2 col3\" >25000</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow2_col4\" class=\"data row2 col4\" >7000</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow2_col5\" class=\"data row2 col5\" >60000</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow2_col6\" class=\"data row2 col6\" >458</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow2_col7\" class=\"data row2 col7\" >14.33%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow3_col1\" class=\"data row3 col1\" >sklearn</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow3_col2\" class=\"data row3 col2\" >25111</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow3_col3\" class=\"data row3 col3\" >22500</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow3_col4\" class=\"data row3 col4\" >9000</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow3_col5\" class=\"data row3 col5\" >58879</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow3_col6\" class=\"data row3 col6\" >168</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow3_col7\" class=\"data row3 col7\" >5.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow4_col1\" class=\"data row4 col1\" >tensorflow</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow4_col2\" class=\"data row4 col2\" >24152</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow4_col3\" class=\"data row4 col3\" >22500</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow4_col4\" class=\"data row4 col4\" >7000</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow4_col5\" class=\"data row4 col5\" >49662</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow4_col6\" class=\"data row4 col6\" >1127</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow4_col7\" class=\"data row4 col7\" >35.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow5_col1\" class=\"data row5 col1\" >caffe</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow5_col2\" class=\"data row5 col2\" >23785</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow5_col3\" class=\"data row5 col3\" >20833</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow5_col4\" class=\"data row5 col4\" >6725</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow5_col5\" class=\"data row5 col5\" >58333</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow5_col6\" class=\"data row5 col6\" >703</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow5_col7\" class=\"data row5 col7\" >21.99%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow6_col1\" class=\"data row6 col1\" >theano</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow6_col2\" class=\"data row6 col2\" >22557</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow6_col3\" class=\"data row6 col3\" >22500</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow6_col4\" class=\"data row6 col4\" >12500</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow6_col5\" class=\"data row6 col5\" >39594</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow6_col6\" class=\"data row6 col6\" >153</td>\n",
       "                        <td id=\"T_e5d2581c_5cc0_11ea_bbe9_701ce71031efrow6_col7\" class=\"data row6 col7\" >4.79%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19231d4c708>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'ml_')\n",
    "data_pl=data_pl[data_pl['head_count']>100]\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9849859243040351"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl['percentage'].values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AGUA+cBVHJbijEumtQLGqVorIU0AlzhSFscC/gX8AV6vqPhEJAB9S1afcYyuBMlW9SkTuxkm6v03OO+4cd4rKBcA1wPuAQm8jMgm0BvgN8Lua62v2eR2M6dlsrd30sg7YJCL/BF4HNgC3A+uBkTjdYMczDpgjIguBYpyl6GqBm4AbgX44CRlg05Ek2s4v3T93kIKLDZQ/Xj4M+CjO+xnhcTgmOWa4X98rf7x8MU6PzL9rrq+xloFJOqv+kl4mActU9XygLxAF7gVudv88kVeBH7ot1K/gJMSP4nTtfhBoabdv83GObznONk/VlpZJbWnZ+XN+MuG3OO/na1gS7Yn8OAPH/gnUlj9e/hn3mbgxSWOJNL1sAz4jIstxFkAvBAKquhN4xe3uPZ5HgAtF5DmgAmdR8n8BdwDV7j6nJTLweKktLSusLS27BXgZeOZ9K2OnYYOHjGMc8CPgzfLHyx8uf7y83OuATM9gz0hNWqgtLRsNfBq4AXirxRHM4vUP3541xqu4TMp7DqgCnrJycSZRLJGalFZbWjYduBu4GJDj7fPNK30bXxztm5jUwEy62QJ8C3jcFtQ38WaJ1KSk2tKyiTjPPd93qn139Wf55z+eNSfxUZkMsBP4NvDzmutrAl4HYzKDJVKTUmpLy8qArwJXcIIW6NEUQh//tP/w4UIZmNDgTCbZA3wXeNjW+TXdZYnUpITa0rIxwD04o4g7PQju2XJ59qGL/QvjHZfJeHXAD4AHa66vafI6GJOeLJEaT9WWlpXgPAO9jm6Mvo342HXNF/3DVMRGopuuOITzDPXHNdfXBL0OxqQXS6TGE7WlZcNx5rR+BGcJwm6ruti3Zkm5b0Y8zmV6rO3AXcCvbXEH01F2926Sqra0bHBtadmPgDeATxCnJApw9ZJYvE5leq6RwC9FdUXwq0Xv8DoYkx4skZqkqC0ty6otLfs8ztKGnyEBi8j3a2La4IO6K97nNT3PvLZAW65STWXRn6ksOtPreExqs65dk3C1pWXzcSbFT0j0tdaNliX3XelfkOjrmMwlqnXLtu/K7a3ax90UAh4A7qXy8GEPQzMpylqkJmFqS8uG1JaW/RpYQhKSKMDkzXpWVlRDybiWyUw3HG6qbZdEwSnU8AXgVSqLrvIoLJPCrEVq4q62tMwHfApnIf0+p9g97n71Dt+yv872zU32dU36y4np5tXbd5b4Tz6C/Gngk1Qe3pmsuExqsxapiava0rJyYAXO4uFJT6IAl62IeXJdk/7uqa8/eIokCs5ylS9RWfQpKovsM9RYIjXxUVtalltbWvYN4AVgppexFAYoH71HX/cyBpN++kWj6y5pbu3o9KneOM9Nn6eyaHwCwzJpwBKp6TZ3MNEG4E7iOJ2lO278Z/RERc6NOZZq7MG9dYVdOPJsYD2VRV+lsijlit6b5LBEarqstrQsv7a07CHgWZxakCljzG6m5AfVlnwzHVIWCi8rD4XGdvHwHJzVuTZQWeRpbwyAiNwgIjd4HUdPYonUdIm7uPxq4GY6uLh8MgkUvn9ZbL3XcZg0oNrywL66ribR9kqBZVQW3WHPTnsW+8s2nVZbWnYjsJYkTWnpqgte0KFex2BS3wUtrWsGR6OD43S6LOCbwL+oLBoWp3N2iYicJSL/EZEiEfmTiDwnIlXtXn9WRL4jIs+4PxeKyD9EZKmI/MLdli8iT7vH/q+IZHn1flKZJVLTYbWlZYW1pWW/BP4HKPA6nlPJjTBm8ubYRq/jMKnLp7r33gMHE9Edey5OV+/FCTh3RwwFfoNTTelGYJOqzgeGishEd5/ZwApVvaDdMQ8A7wRKRGQwMB6Iucf+AujKc+SMZ4nUdEhtadkknFboh72OpTOu+79Ys9cxmNRV0XD4jXzVRN0UDgD+SmXRA1QW5SXoGifyKWAXztrB44DLRORZ4AzgNHefTar6VLtjwsBNOAm4H5APrAM2icg/gQsAq916HJZIzSnVlpbdDKwkxQYUdcRp9UwvatY6r+MwqScvFnv1Ew2Nc5JwqU8Bq6gsKkvCtY64F2f8wr3Aq8APVXUhTsWlHe4+R99kfhT4E04rtsXdNglYpqrnA32BeYkNOz1ZIjUnVFtaVlRbWvYH4CEg2XfUcSGQc+1/Yi97HYdJPd+sq2/xJe8zcCLwApVFH0nS9QKquhN4BdgKXCgizwEVwIlWZPoXcAdQ7f58GrAN+IyILAeG4PRKmaPYEoHmuGpLy6YDv8fpCkprUR+7PmRFv007gyORNf/eudur2rUPAp+j8nDEo+ubOLMPFnOM2tKyzwHLyIAkCuCPMXz+Jn3B6zhMilCNPLivrp+HEXwKeIbKIi9jMHFkidS8xV3m77fA93EmmWcMK/ptjpgUDC0vDYVHexzGucAaKovO8jgOEweWSA0AtaVlA3GejXzQ61gSwYp+GwBUG3+0ry5V1sY9A1hBZdElXgdiuscSqaG2tKwUZ1RuMkYwekLAd+O/Y5u9jsN465LmlnX9Y7EBXsfRTm/gz1QWfcXrQEzX2WCjHq62tOxc4Emg2OtYEi0GB679or9PxC8Z1W1tOsavumvV9p0DcjVlR6D/AbiRysM2VzPNWIu0B1s69wMfBP5BD0iiAD4YcOEaXeN1HMYbnz3UsD2FkyjAlcB/qCxKpRaz6QBLpD1UVUX1XRsnVDxyqOjMHlW304p+90y9YrGXbzjclA6PLmbiLHxf4nUgpuMskfYwVRXVvqqK6oeAryHSa/3kzw5szR94ognaGceKfvdM39l/ICIpWKXoBMYCy6ksmnjKPU1KsETag1RVVOfhLAF281sbxTdw1YwvR8JZvQ55FliSWdHvnmV4OLJyXlsg3ZLSUOA5KotsSb40YIm0h6iqqC4C/glcdvRr6ssetWJW5c6oLyuQ/MiSz4p+9yCq4ap9+9O1nF4RzsINF3odiDk5S6Q9QFVFdR+cJHrCu9tIdsHE1TO+sl6RjF+5QKDw/c9b0e+eYGYguPyMcGSk13F0Qz7wFyqLrvQ6EHNilkgzXFVFdW/gGZxBDCfVlj/w7BcnfXpp4qPy3gXrrOh3phPVhu/tPzDJ6zjiIBt4gsqij3odiDk+S6QZrKqiuhBnesvsjh5zqO+4Ba+e+YEliYsqNVjR78x3ZVPzhuJYLFOmdvmAR6gsusnrQMyxLJFmKDeJ/p0urFb05mkL5u0aNm9l/KNKLVb0O3NlqW7/Yv2hs72OI84E+CmVRdd4HYj5b5ZIM1BVRXUv4G/AOV06gYjvtTFXTT7Yt7QmroGlGCv6nbm+VH9oT06GFV5w+YDHqSy6wutAzNsskWaYqorqAmAx3a1kL5L34sRbhrUUDNkWj7hSkRX9zkx9otGNVzc1d/hxRhryA7+lsui9XgdiHJZIM0hVRXU+8DSwIC4nFF//1dPvkFB27wNxOV8KOudlHS2qGT9SucdQ1R/sP+D3OowkyAb+SGXR+V4HYiyRZgw3if4VeEc8z6u+rJErZt2zL+rLyciFtK3od2YZFY6smBkI9pQan7k4lWPic+NsuswSaQaoqqjOwqngcl4izh/Nyj9r5cy7NikSTcT5vWZFvzOEaqBqX90Ir8NIMqcXqrIo0wZWpRVLpJmhCkjo6ifBvH4z10353LJEXsMrVvQ7M8xvC6w8PRIZ7nUcHnBG6FcWTfA6kJ7KEmmaq6qovh34eDKudbho9Pzacdc8m4xrJZMV/U5/onrgvv0Hpnodh4eKcFqmg70OpCeyRJrGqiqqrwC+ncxr7hly9oIdw89dnsxrJsPkzXpWVlRDXsdhuub6w00v91bt6SXyRgJ/pbIo3+tAehpLpGmqqqJ6NvArkl0aSkTeGH35tAP9yzck9boJ5oMB71mja72Ow3ReTkw333qoYa7XcaSIGcCvqSxKl5JxGcESaRqqqqgeBfwFyPMkAJHcjRM+MbKp12kZ1R166fJYodcxmM67u/7gQb8zt9I4Lgfu9zqInsQSaZqpqqjui7Nq0SBPAxEpXjvtS7mBnOJ9nsYRR4VBJlrR7/TSLxpd977mlhlex5GCbqOy6BNeB9FTWCJNI1UV1dnAU0Cp17EAqM8/fOWsuw9F/LkZs2atFf1OI6qxB/bV9fI6jBT2oC3YkByWSNPLo8BCr4NoL+bPLV058+5XYuKLeB1LPFjR7/RRGgovnxgMjfM6jhSWhbP6kU2LSTBLpGmiqqJ6EXCd13EcTyi3ePoLU29f4XUc8WBFv9OEassD++rO9DqMNNAHpzB4ppSTS0mWSNNAVUX1fODrXsdxMk29R8zbVHbjs17HEQ89oeh3qD69Z/q8q7VtzZBodIjXcaSJM4BfeB1EJrNEmuKqKqoHAr8lDUYl7h88feHWke9+3us4uitdi35HDkfY8s0t/7UtsCvA1u9sBWDvH/ey7bvbUFVaXmnxIsS48Knu+3pdvQ0w6pxLqSz6gtdBZCpLpCmsqqJacOaKnuZ1LB21teTiWfsHTlnndRzdlW5Fv6MtUXY9sotY8O11g1WVPU/sQSPq7NMUJW9kHoHtAbL7ZXsVard9vKHxtQJVG2TUefdRWTTH6yAykSXS1LYIuMDrIDpFJHvT+I+e2dh75Gteh9IdaVf02wenf/J0fHlv/5duWNpAYdnbU2NVFaLQ8moLvUrTMw/lxWKv3txw2BZf6Jos4PdUFg3wOpBMY4k0RVVVVJ8D3Ot1HF0i0mft1Nt6t+X22+N1KF2VbkW//fl+/AVv9/5HmiM0rGhgwLvf/szMG55HqD6E+ISt39pKYHfAi1C75Rt19S0++9zqjuHAb6gsst9hHNkvMwVVVVT3B54gDZ6LnpD4hq6aeVdz2J9/2OtQuiqdi37v+8M+Bl8xGMl6e6W4ARcMoHhOMZIj9JnWh+YNadV7zaBIZM35rW09eWH6eDkf+IrXQWQSS6Qpxn0u+kucO8e0FvPnjFk5654tMfGFvY6lK/wxhs+vSc+i3y2vtrD3D3vZ8q0tBHYE2PekswBVtDWKP8+PZInT1ZsuVKMP7qvr53UYGeQeKosSUr+4J7JEmnpuB97jdRDxEs7pPWXNtDtWex1HV6Vr0e+x3x7LGXecwRl3nEHeiDwGv38wwb1B8kfkk39GPgf/fZBe49LnOenEYGhZWSg82us4MogPp4vXnpfGgaTVXWmGq6qongMswRkUkFEGHNjw7MRNP1vodRydpRD7zCf8u/f1k7TvIUhbqk3/2flmYEA0NtDrUDLQk1QevsLrINKdtUhTRFVFdQHOVJeMS6IABwZMWrh51HuXeh1HZ1nRb++9t7nlBUuiCfN+Kouu9TqIdGeJNHXci7MCScbaPuKCOXsGz1zjdRydZUW/veNXffOe+oOzvY4jwz1AZZH1uHSDJdIUUFVRPRP4rNdxJJyIv7b0uvENRaNrvQ6lM6zot3c+c6hhW656VHe3h1Al96HIJV/2Oo50ZonUY25ptEdJ56kunSHSa93kW/u35g/Y5XUonWFFv5OvVyz28o2Hm2wlngRq1IKaC0Lf3nt/5OqKkkWLb/Q6nnRlidR7i4Byr4NIKvENWjXjK6FwVkGD16F0lBX9Tr779x8IC8ip9zSdpUrgd5F3LJkU/NlZr+npo9zN3y9ZtDjjCzYkgiVSD1VVVJfRQydGqy/7jBWzKrfHJCvodSwdZUW/k+e0cGTl/LbAJK/jyERNmv/Se0LfenNR5GMLFF/7HFAM/MSruNKZJVKPVFVU+3C6dHO8jsUrkexek1bNuPMFhaTOwWqJRWmKRjt9nBX9ThLVcNW+/VYiLc5UCT0ZnbdkUvCR0lodeaI5ue8rWbT4qqQGlgEskXrnFqDHP/9pKxg8Z8PEW56L93kjqpy7+Q2u37Gd63ds51eHDvLerVvYEw7zXHMLub7O/9O3ot/JMSMQXD46HCnxOo5M0qJ5tZeEvr79C+GbF8TwnWo8xgMlixb3T0pgGcISqQeqKqpHAN/0Oo5UcbDf+AWvj37/knie87VgkIv69OHxESN5fMRItoZCfGHgIF5sayOsSo507dFbTyj67SVRbfj+/gMTvY4jU6gS/kv07CUTg4+MqdEzxnTwsIGka8EMj1gi9cZPARsF2s7O4e+Y9+bQuavidb4NbW0829zMVdu38ZW9e/ABAY3xQlsrMwoKugdtT2YAACAASURBVHze3AhjpryRfkW/08UVTc0bimOxvl7HkQlaNffVy0Jf2/LZ8KcXRPF3dqGXj5csWnxWQgLLQJZIk6yqovr9wLu9jiPliPheHfvBiQeLx22Kx+km5OXxP6eP4PcjS4io0s+fxa8PHeL07Bzur9vP041dL0pzXXV6Ff1OF1mq2xfVHzrb6zjSnSqRv0VnLpkYfOSMF/XMcV08jR/4fjzjymSWSJPInTN6n9dxpCyR/BcnfWpoS8Hg7d091bjcXAZmOTfhZ+Xlke8TvjN0GEOyszg9O5uVra1dPvewdCv6nSZurz+0O6cHD76LhzbNef39oco3Phm+dUGErOxunu78kkWLL4pLYBnOEmly3Qyc6XUQKU18/VdPv5NQdmF9d07zpb17eCUQIKpKdXMz43LzWNPaytT8AvxItyYnplvR73TQOxrb+KGmZmuNdpEq0X9Fpy2ZGHx05DodWxrHU3+vZNHijFz/O54skSZJVUV1EXC313GkA/VljVwxq3JP1Jfd1tVzfLL/ABbt2cPl27YxKS+f2QUF5Pt8DPD7eTHQxtjc3G7FmM5Fv1OOqv5gf13PWNkrAQKavfmq0F2vfiz8hQVhsuLdoh+HM8PAnISVUUuSqorq+4AveR1HOslrq1919qp7Zgiakjd8VRf51iyZ6JvhdRzpriQUXv7XN/f0+KlgnaVK9NnYpOc/Ef787BDZ3bszPLlDwJnb7rvoYAKvkdZS8gMq07jTXTJ/Ufo4C+T3n7V+8mdTtvRauhb9TimqgYf21Y3wOox0E9SsrdeG76y9MfylBQlOogB9gcoEXyOtWSJNjm+AVbDoiobiMQteGfvBuM4xjZd+zUwbfFDTavH9VDOvLbDy9EjESnh1kCqxpdEJSyYGHx26LDZhQhIvfXPJosXxfPaaUSyRJlhVRfVU4Bqv40hnu4fOnb/ztIUrvI7jaFb0u3tE9cC39x+Y6nUc6SKkWduuD39p04fDdy4IkpPsG/MsbDrMCVkiTbzvYBUsukdEXj/ziqkH+p21wetQjmZFv7vuusaml3ur9knW9fY1xwhH029MiCq6Mlb23KTgI4Oei03yctWnC0sWLbY58MdhiTSBqiqq3wOc63UcGUEkd2P5zSObew3b4nUo7VnR767JUd1y68GGuA8w+smaEAsfa2HhYy1MfrgZ/9camfFIMy0h5ZnNEbL96XVPG1b/jo+Gb9t4deiu+W3kdn1Jrvj5mtcBpCIbtZsgbnWXDUAyn2NkPIlFd81Z+ZXc3FDjQK9jOaI5l40f+XyWrQ/bCffW1a++tLllZiKv8em/tVF7IMYHxmczboCP+lbl/eO7u0ZBcqiiL+jYpdeFFk1rJa+X1/Ec5fxt9130L6+DSCXWIk2c92FJNO7U5x++cuY9B6K+nBavYzmiMMjE0bv1Na/jSBd9o9H1iU6ibzbG2NeijCzyEY7BPzdHuHBMeqwrEFHfrorwrS9eEaqcn4JJFOBOrwNINZZIE8fmjCZINCuvbOXMu19WpPNFRRPkxn9Z0e8OUY09sK8u4V2UVWtC3Dw9h/NHZ/H0a2GG9/FxyROt/GdrJNGX7pYXY6OXTgo+UvRMbOYUr2M5iYUlixbP9jqIVGKJNAGqKqoXALO8jiOTBfP6zlg79bblXsdxxJjdTLWi36c2LhRePikY6upC6h0SU+U/26IsLPFz1YRsKhfmUpwnXDQmiydrw4m8dJdF1Lf7ltBnXrg0dO+8FvJ7ex1PB1irtB1LpIlhrdEkaOpTMu+l0uue9ToOsKLfHaLa+uC+uoSvNb10e5RZp/kRt+bs6/UxRvcVcrOEWAoOCdkUK3l+cvBnhYtjs6d5HUsnXFyyaHG510GkCkukcVZVUT0RuNDrOHqKfUNmLdx++ruWeR0HWNHvU3lna9vqIdHokERf55nNEeaPdJbubQwqQwp9jB/o52cvhHjnGanznDSqvj23hj659uLQN89ppiBp04DiRIBFXgeRKmzUbpxVVVT/GluAIblUwxNeemTToAMbPH+u9K0P+DauP9NnI3iP4lPdt2L7rsIC1VQcPJN0tbHTl10VumtCI4VFXsfSDVFg7Lb7LkqpKWlesBZpHFVVVI8ErvI6jh5HJHvTWR87o7Hw9Ne9DsWKfh/fxxoaX7MkClGVfbeFP7H6wtC356Z5EgWn+Lc9xsISabx9AWcpLZNsIkVrp91eGMjtu8fLMKzo97HyYrHXPtlweK7XcXjttdhpy6cGf5r7p+iChE79SbLrSxYtHuZ1EF6zRBonVRXVA4CPeh1Hjyb+oStn3t0U8ec1ehaCFf0+xr0HDjb5evBnTVSl7o7wR1edH/rOnMMUFnsdT5zlAp/3Ogiv9dh/3AnwKSAVlvDq0WL+nLErZ93zekx8ns1zsKLfbxsYiax9d0trOo1GjavNsaErpgd/4n8iel4mT4f7RMmixZl2g9AplkjjoKqiOg8nkZoUEMrpM23ttC+t8ur6/hjD59foC15dP2WoRqv21fX1OgwvxFQO3B2+fsV5oe+dfYg+/byOJ8EKgQ95HYSXLJHGx+VAf6+DMG9rLhx+Ts1ZNz3r1fWt6DeUB0PLykLh0V7HkWzbY4NWzgg+xC+jF5ztdSxJ1KMfa1kijY+PeB2AOVbdwCkLt5RctNSLa/drZtqQg7rTi2unBNWmH++vK/M6jGSKqRy8N3ztigWhH86up2iA1/Ek2dSSRYsnex2EVyyRdpM75cVKpaWobSMvPHvvoOlJL3Mm4LuhBxf9vril9YUB0VjKVOhJtF06YNXs4IPRn0ff05NaoUfrsa1SS6TddyNWuDt1iWS9XHZDaUOfUa8k+9KTN+uEnlj026+6+54DBzN5cM1bYkrDfeGrl58T/PGs/fTtMTcOJ3BNyaLFeV4H4QVLpN1QVVEtwA1ex2FOQaRw3ZTP923LG7ArmZftqUW/P33o8NY81Xyv40i0PdpvzZzgg8GHo5fEvUB5muoLXOZ1EF6wRNo95wEjvQ7CdID4Bq+a8ZVgOCv/cDIve+nyWGEyr+e1gljs5Y8cbszoxKLK4e+Fr1h2dvDBGXvpN9jreFLMTV4H4AVLpN1jg4zSSMyfPXrlrMqtMfEnrbu1pxX9vn//gbBk8KOOfVq89pzgj1ofiF7e41dqOoF3lCxafIbXQSSbJdIuqqqoLqaHdmOks3B24eTV0+9cq5C0ag09pej3sHBk1YK2wCSv40gEVRp/HLn0+VnBh6a/yUCr8nNigjNupEexRNp1HwJ65IP1dNfaa8icjeU3L0nW9XpE0W/V8EP79mdkN+cB7bNufuiHTd+PXHmO17GkiRtKFi1O2dwiIgtFpCSe50zZN5sGeuxQ70xQ33/CwtdHX/ZcMq7VE4p+Tw8El48OR0q8jiOeVGn+SeS9S6cHH566Uwed5nU8aWQ4cIHXQZzEQqAknie0RNoFbvHuqV7HYbpn5/Dz5u4eMnt1Mq6V0UW/VQ9/f/+Bcq/DiKd67b1+Yej7Dd+OfHCe17GkqbiNHxGRF0Tk7yLyFxFZJSJfFpHvikihiKwTkd4i8piI3C0iS0VkuYjki8hg97jlInKHe65f4My0+KGI/CZeMVoi7RqrOZoJRPyvjLt2wqHiMS8l+lK5EcZMeSO2MdHX8cIVTc0v9o3FMmI9WVVaHo1c+Ny04MOTt+uQ4V7Hk8beU7JocbymQBUAHwAm4jxSOxOYAPwI+LbqW49NClV1HvAKMAW4A/i9qs4BLhWR/qp6I/AYcKuqXhOn+CyRdpENMsoUIgXrJ31mUGv+oB2JvlQmFv3OUt1xR/2hjFjNp0F7bTgv9N0DX498eD5Ixo48TpIC4te9u09Vm4HtQBRnQNOjwMXAH9vt97j75w4gBxgH3CwizwK9gITVTbVE2klVFdWlQI9aQzTjiW/gqhlfjoayex1M5GUysej3bQcPvZnjfGilLVXaHo+867kpwZ+Wb9FhNi88fhLV4MgCPgP8Cril3faWo/Z7FVikqguB+4Aj/7/bcEteisTnhskSaedZazQDqS9r1MqZlW9GfVmBRF3DLfqd8G7kZOkdjdVc09ic1q3Rw1pQc37o/n33RG6cr/js8zC+3luyaHFWAs57KfA0TtfttSJyoi74+4DbRGQZ8G5gn7v9SWCRiKwE4lKdSFSTNp0uI1RVVK8Bpnsdh0mMvLYDK89eVTlT0IR8qEZ97PrQF/3DVCS9P7RV9dG9+1+eFQie5XUoXaFK4Inouau+HPnIPEugCfWubfdd9G+vg0g0+wfUCVUV1cOxJJrRAvkDZq+f9OmElV7LlKLfJeHIinRNok2a/9KFoft23xm5aYEl0YTrET149o+ocy7yOgCTeA19xy14dcyVCVuwIe2LfqsGq/bVjfA6jM5SJfjHyPwlk4KPlL6iI3rcMnYeudjrAJLBEmnnWCLtId4cNn/ermHzVybi3Ole9PuctsCKEZFIWk0Nada8ly8OfWPn7ZGKBTF8fq/j6UFGlCxaPMHrIBLNEmkHVVVU5+FUezE9gYjvtTFXTq7vWxb3uZ/pXPRbVOu/XXdgitdxdJQqoT9H5yyZFHxk7Es66kyv4+mhMr4BYom04xbiDpk2PYRI3oaJtwxvLhi6Nd6nTtei3x9ubHqpT0yLvI6jI1o195VLQ1/bdmv4Uwui+BMxetR0zHu8DiDRLJF2XMbfVZnjEOm3ZvoifzC7d1znf6Zj0e8c1S2fO9iQ8rVGVQkvjs5aMjH4yOgNeuZYr+MxzClZtLjY6yASyRJpx73b6wCMN9SXNWLlrMq6qC+nNZ7nTbei3185cPBAljMZPmW1ac5r7w9Vbr4l/NkFEbKyvY7HAM6/mVRexL7bLJF2QFVF9WCc9R1NDxXNyhu/cubdmxSJxuuc6VT0u280uv6y5paZXsdxIqpEnolOWzIx+GjJOh1b6nU85hgLvA4gkSyRdsxcrwMw3gvm9Z35wpTPL4vnOdOi6Ldq7Mf76uK1AHncBTT7jatCd732ifAXFoTJSuvlCjNYyj8S6A5LpB1jidQA0Fh0xvzacdc+G6/zpUPR77Gh8PLJwVDKtfJUiVZHJy+ZGHz09NVaNt7reMxJTShZtLi310EkiiXSjrFEat6yZ8jsBTuGn7c8HucSKLwilYt+q7Y+uK8uLuuRxlNQs7ZcE77zlY+Ev7ggRHau1/GYU/IDs7wOIlEskZ5CVUV1PlbE27QnIm+Mvmx6Xf+JL8bjdOencNHv81rbVg+NRlMmPlViz0XLl0wMPjpseWxCWi5R2IOldYGDk7FEemozARv9Z/6bSE7NhI+Paio8/Y3unipVi377VPd/o64+ZdaWDmnWtuvCi166LnzHgiA5eV7HYzotY5+TWiI9NevWNccnUrR26u35gZzifafe+eRSsej3TQ2Nr/ZS9XyKjiq6Ijr+uYnBRwYvjU0s9zoe02WzSxYtzsiC6ZZIT80SqTkh9flPWznr7oaIP7dbA4ZSreh3biz2+i0Nhz3/tx9W//aPhm/b+MHwV+YHyE3ZkcOmQ4qBjBwUZon0JKoqqoUM7tc38RHz545bOfOe12Lii3T1HKlW9PveAwcbfR5+Pqiiq2PjnpsYfHRgdWzqJK/iMHGXkZ+nlkhPbjzQ1+sgTOoL5RZNWzv1i92qFnPOy3qmqHpeY21gJLL2wpbWaV5dP6z+nR8Pf37DlaF75reRa+tbZ5aMfE5qifTkPO/aMumjuffp52wa/5Eu1zFNiaLfqtEH99V5si6qKroudubSycGf9f1XbPpkL2KIt2jLITTa5Y6KTGSJtAdKmRGLJj3sHzRtwdaRFz7f1eO9Lvo9IRRaPj4UTvpymBH1vXlL+LPrLw99bV4L+Z4PcOqMcMNe9v+xkr2/+SIHqx+l8YW/sufxzxELBWjbug6xwjPtjS1ZtLif10HEmyXSkyvzOgCTfraWXDRr38CpXWpZelr0W7Xpx/vqkr6C0cbYqKWTgz/r/bfYrLScr93w7GMUzbmaIdfcT7Spnqa1/4/CSecT2vsakm2zdI4iwAyvg4g3S6QnZ4nUdJ5I9kvjPzL2cO+SVzt9qIdFvy9qaX1hYDQ2MFnXi6hvz2dDt6y9JPSNec0U9EnWdeMtfOhNcoY4iz/5C4pQjaHRCG1b15N/hmePmlNZyi032V2WSE+gqqJ6INDf6zhMmhLp/cLULxS15fXb3dlDJ2/WCVkRDSYirBPxqe6pPHAwaUu4vRQb+fyU4E8L/hKbm/aPTwrGzaVh2RO0vrGKtq0vUDz3Q7RtXkNW7wHUPXkvge0pt9aG1zKuRqwl0hPLuLsmk2TiG7Jqxl0tYX/+4c4c5oMB71mb3KLfnzp0eEueasLnaUZV9n0hVLHmotC3zmmiV1Gir5cMxXOuJv+MaTRv+Ce9JpxHYfl5FM/9EL68XuSPnkHra3EtGJQJLJH2INata7ot5s8Zs3LWPVti4g915rhLl8eSVimjIBarvelwY8JHU74aG75savCnuU/G5mfcM7KcQWcQaayjz4xLAQgf2k1W8VDEn42qehxdyhnndQDxZon0xCyRmrgI5/Sesnr6HWsUOvyJmsyi39+uqw+KMwgkIaIqdYvCN626IHT/3MMUejK1JtEaVz1FnxmX4svOIxZsxd+rL9n9T6dpwz/IH5kRM3niaXjJosUZtUqVJdITs65dEzetvYbO3TjhE8915phkFP0eGo6sWtjalrBP+jdiQ5dPD/7E/7vouRlbQgugeN41FE44FwBfbgH5o6bgyy1g2I0PUDAuI6dOdocAY7wOIp4skZ6YtUhNXNUPmLhg86hLlnZ0/4QX/VYNP7SvblAiTh1TOXBX+IaV7wx9b84h+mTcvEHTbRn1nNQS6XFUVVQXACO8jsNknu0jzp+zZ/CsNR3ZN9FFv6cFgsvPDIdHxfu822KDV8wIPsSvoufPjve5TcawRNoDjCOBz4xMDybiry398PhDRWe+3JHdE1b0W/XwD/YfiGtJspjKwXvD165YGPrB2fUUDYjnuU3GsUTaA1i3rkkckV7rJ392YGv+wFOuYJSoot/vb2p5sW8sFrcu152xAatmBx+M/jz6noys7mHizhJpD1DidQAmw4lv4KoZX46Es3odOtWu8S76naW68876g3Hpdo0pDfeFr14+L/TjWfvpm7RVkUzay6gpMJZIj2+w1wGYzKe+7FErZt2zM+rLCpxsv3gX/f7CwUO7ciC3u+fZrf1Wzwk+GHw4eokNSzWd1a9k0eKMWTnOEunxJWQkozFHi2T3mrh6+pfXn2yOaTyLfveOxmqubWzuVverKoe/G/7A83OCD87cSz+76TRdlTEDOi2RHp99OJikaSsYdPaLEz990jmm8Sr6/f39dd0aRLdX+66dG/xx64PRy87pbiymx7MWaYazRGqS6lC/0gWvnfmBExYF98cYvqCme+vvjgyHl88OBCd05VhVGn8Uuez52cGq6bsZkJiRxKansUSa4axr1yTdrtMWzHtz6DkrT/T6VUtiXW9NqgYf2lvXpa60Oi16YV7oR80/iHzAWqEmniyRZqqqiuosMugv2KQREd+rY6+edLBvac3xXu5O0e+5bYEVIyKR4Z05RpXmhyKXLJ0R/Mm0XTpwWFeua8xJZMznrCXSYw3EFmMwXhHJf3HiLcNaCoZsO+alLhb9FtX6++sOTOnMMfXae/3C0Pcb7o9cPa+z1zOmgzJm6UhLpMeybl3jLfH1Xz39Dgll9z5w9EtdKfp9TWPTpj4x7VDtT1VaHom857lpwYcnb9chnWrBGtNJ1iLNYDbQyHhOfVkjV8y6Z1/Ul9Pafntni35nq279wsGGuR3Z95AWbjg39N36b0SunQ9ivTIm0SyRZjBLpCYlRLPyz1o18ys1ivzXtJfOFP3+yoGDdVmQdbJ9VGl9LHL+c1OCP524VYdlzNw+k/IskWYw69o1KSOQ13/Wuimfe779to4W/S6ORl+8vLll5sn2OawFG88P3b+/MnKDtUJNslkizWB9vA7AmPYOF42eXzvuQ8+233bKot+q+qN9dSdcBlCVwG8i5y6ZHPzZhNd1eElcAjWmcyyRZrBsrwMw5mh7hsxZsGP4O5Yf+flURb/HhMPLpgZDx61i1KT5L707dN+eL0duWqD47DPAeKW4ZNFiv9dBxIP9JzqWJVKTekTkjdHvn3ag/4QNcIqi36ptD+6rG33sZoJ/iCxYMin4SOmrOiLuBb2N6SQB+nodRDxYIj2WJVKTmkRyN06oGNnU67TNcOKi3+e2tq0aFon+12vNmvfyRaFv7vpi5BMLYvgyohVgMkKB1wHEgyXSY1kiNalLpHjttC/lBnOK9rtFvze0f9mnuv+bdfXTj/ysSuh/o3OXTAo+MvZlLTmmlWqMxzLips4S6bEskZqUpj7/8JWz7qmP+HObr6uOtbR/7aOHG1/tpVoI0Kq5r7wvdO/2z4VvWRDFf9IpMMZ4xBJphrJEalJe1J9btnLm3a8MOeibfKTod24s9vothw7PUSX8dHT2kvLgo2du1NFjvI7VmJOwRJqhLJGatBDKLZ6+buptLxwp+v21AwcPhzRn8+Whr275VPgz1go16cASaYayRGrSRlPvkfPysz8SHBiKrPY1jm8pDz46ar2OGed1XMZ0UEYkUrtjPZYlUpNWmoumXnDxqp1P7ux7ds7t2aF/NcjhWLO0+QIS8IcllKfEskBt1SKTcho03+sQ4sIS6bEskZq0ohoNadPm9zY2bc4qKZyw9ty+8315/sJpIs6Sf2EiLS0SPNAkbY2N0tbSJG2hJmmLNUswq01CuSEivaNEixUGIPaZkGjNzc3k5+fj92dEY6xb+hLIiBs8+09zLEukJq1oZPcbwHiAbc2bZm5r3kTv7H7bp/Z/57bBeSWTsiWruFizehVrr5OfB421ETrQLIFDTRJoapLW1kZpizZJgFYJZgckXBAm0ieGDkBsKc2TCQQCPPnkk8RiMXJycigpKWHjxo1cf/31bN68mUmTJnkdYqqInXqX1GeJ9FgRrwMwpjOi4S3H1C1tCh8cuWTvH0b6JauttGjW0nFFMwdk+3KOu2TgEYL4CsgdUKC5AwadonxphGigRYL1zRI41CitLY1uK7dFgr5WCeWGCBdGiBWrk3RzuvkW005NTQ2zZ89m9OjRPP3006xcuZK5c+eye/dusrPtXr0dS6QZ6oTrlxqTimKR7SfsI4xqJP+lhmXzXmpYxqC8ES9N6f/OhqLsAdNF5IQL2ndEFv68Ii04rUgLTjuNfifdN0DoULMEDjZJoLFR2tqapDXcJAFaJJgdkFB+mGjvKLF+yClOlEZmzJjx1vetra307duXWCzG5s2bmT9/voeRpZyo1wHEgyXSY1kiNWlFo4eGdGS//YEdZz3z5v+Q6ys4MKnfwpUjC8eP9ol/eKLjyyOnb57m9B2gJ+8NjhILtUqwvpnAoUZfW3OjtAabJBBrloC0EcwNSqQgTLSv28rNS3Tc8bBz504CgQBTp07lxRdfZNy4cTzxxBPMmzePUaNsuWOgzesA4sES6bEskZq0oRpsgminPpGDsdYBqw/8bcHqA3+LjSosX13ed74vz9/rrcFJXvHjy+mt+UN7kz90aPTka5kHCTe2SLDeHUDV2iht4WZp0xYJ+tsklO8MoIr1A/oh3kzza2tr4+9//ztXXnklxcXFFBcXc+jQIcaMGUNtba0lUkdrPE8mIrcBTar6U/fnzwENqvqLdvt8E1iqqn8XkVHA11X1mu5c1xLpsZq9DsCYjoqFd24GJnfxcN/W5pqZW5tr6J3d3x2cNHKSiBTHM8ZEyCW7T65m9+nnrIZ4QjFikVZCdc4AqrbmRl9boEnaIk0S8LURzAlIuCBCtMgdQHXy0VidEI1G+eMf/8h5551HcbHz66yvr6d///4EAgFUNV6XSndxS6QishgYBYRE5L3u5lFAVETOU9VrRcQPXAD8UEQuBZYB4e5e2xLpsaxFatJGLLylIR7naQrXj1yy9/cj/ZLVVlY0e+m4ohkDs3w5pfE4t5d8+LIKyRtcqHmDh2jxSYe2uNOE6puk7XCjtDU3Slu46e1W7pFpQn3daUInnbuybt069uzZw9KlS1m6dCmTJ0+md+/eDBw4kKeffpoFCxbE+62mIyWOXbuqepGI3AoMBP7ubv4AsF5VH3N/vhJ4GaeE24eA54HhIvJtoERVr+rKtS2RHssSqUkbsciuuD4rjGokf1PD8/M2NTzP4LyRm6b0P+9wnzgMTkoH2WT16sQ0oSOt3KZGeauVS6s4rdxZ02f2mTF9Rj+EY4Y/V1RUJOw9pJnmysrKRDTN84AjXRVv/bt1/w3fBrwETARmAI8BfYA/Ad/r6gUtkR7LEqlJGxo7nLDBQvsC2yf8463BSe9YMbJw/Bif+E5L1PXShTtNaGCB5g7swDShNmeaUFtDo7S1NEpbsEnatFkCPreVWxh5u5Xb0+bFNCbovBcB09zvhwMr3e9nAn/A6dq9DFgPfBz4LvAi3RhBbIn0WPaM1KQFjbXUgSZ81K0zOGnxwjUH/hYtKSxfXd53nj/P32uq14OT0kEW/vwiLRhepAXDT3YHoqgGCR+ZJnS4UVoDzgCqAC0SzApIqCBMtI87TejkI7HSR9wSqfvs80jH/X1HunLdrl5ExIfzPLQWKAPuBh50988CHgVe6Or1LZEey1qkJi3Ewtu24TwPSgpF/VubN87c2ryRPtn9t03t/67tg/JGTBaRkzfLzCkJIp2cJnSgSQINTXJkmlBbtFkC/lZCOUGJ9IoQLU6DaUKH43iu64ArcJ67iohc4W4Xd9tVwEPAiqOOy3L3+RyQJSL9VbW+sxe3RHosS6QmLUTDW+I6daAzGsP1Jc/u/V2JX7Jay4rOXjquaPqgLF+OVZ1JAnea0LDemj+MUzROnWlCgXp3MYyWxrcXw8gKSCiv3TSh/gjJ7mE4ZkWurnKnt/xCRG4HBqjqlwBE5Cngn6r6r7pdDAAAIABJREFUsPvzAPeQKFAE7AduUNWwiHwW6Afc09nrWyI9liVSkxZikd1xm67RVVGNFGxqWDpvU8NSBueXbJrS77zDfbL7zxCRHrcsYCp6e5pQ75Pu504Tqne6ltuaG31twUanlSttBLMDEi50pwn1j+M0oX1xOg8ikgc8gTOQ6M52L30I+K6IPKaqNwA5QJ6qHhKR14BnnMMFoAH4ZFeub4n0WPu9DsCYDtGWlJrRv69t24R/vPlz8vy96ib1XfjyiMLxZ9rgpPTQyWlCzc1OK/dwk7MYhrvOciCrTcJ5ISKFHZwmFLfPWlUNiMjnVHXb0duBT4nIYPfn3cDV7vefjdf1xSYGH6uqoroROPktnDEeikUP7Qw1/uJ0r+M4GUGiowrL107oOz8rz19gg5N6GHea0EG3ldvYKG3BRmmLNEtAWiWY7cf3i8987bZHvI4zHqxFenw7gLO8DsKYE4mFt+4CUjqRKurf0rxx1pbmjfTJHrB1av937rDBST1HB6oJ/TDZMSWKJdLjs0RqUlosvCXkdQyd0Rg+MOrZvb8b5Zes1vHFZy8d22fGoCxftg1O6tl2eR1AvFgiPb4dXgdgzMnEovvTci5hVCMFNYeWzqs5tJTB+SU1U/qd12iDk3qsN70OIF4skR7fdq8DMOZEVGNRNDDa6zi6a1/btvK3Bif1e8fLI3qVjfGJb5jXcZmkUGC310HEiyXS47MWqUlZGt2/BRjjdRzxEoi2DFxV9/SC1XWLo6N6T1xV3ndedq6vYEomDU5qDrYSI0af3JNXq+lB9g+/b163q66kCkukx2ctUpOyouEte8mgRHqEov4tTRtmbWnaQJE7OGlgGg1OisQizH34akYUO43qd4+dx29f/Cu/uvI7rH1zE+8eO9/jCFNKxnTrgiXSE7EWKdASaGTHgdc4vf8YCvNP/lkWCLWiGiPf7rgTLhbenvFz1g6HD4z6z97fjfJLdoszOGl6yg9Oqt2/mfeNP487F94MwJ3//D53LKxg7ZubiMSi5Ph72pr0J7XN6wDiyRLp8b0JROjBv5/WYBMP/+PLnDViNk+teJjPXPxd7n/qZvr3HgrAB+Z+ivqmPfy/1T/nlvfcx5a9LzFp1DkeR90zaPTAIK9jSJaohnvVHHpuXs2h5xiSP2rjlH7nNffO7jc9FQcnrdv9Mv/3xgqW71hP6YAzyMvOJRAJsmZXDTfP+qDX4aWal7wOIJ5sQYYTqKqo3g6M8DoOr7y+ewNZ/mxGDR7PUysepnd+ES2BJi6d/fG39vn90h8xfsRMQuEA0ViEmWPf5WHEPYNqJBhs+LEPelzJrbe4g5NeGtGrbGwqDU56cU8tQ3sPZHDhAG59+huU9D2N57at5cKx81n35kucP/YcLhtv/0dcVw2/b94fvA4iXnxeB5DCevRz0jHDJjFq8Hje2L2R7ftfIdufy6YdK/nOU5/kN89+h2gsiogQjoTYvHcTY4ZN8jrkHiEWefMNenAShbcGJy3807bvDl574JmVgWjLeq9jAigbOJrBhc6a6BOHjCM/O48H33sXw3oPYkTxUJZt63KVrky0yesA4skS6YnVeh2A11SVFzb/h4Lc3gwfcCafvug73H75Q0RjUV7asYqpZyxkyaanGNBnKE+teJg1r/+f1yFnvFh4S9wqZqQ7Rf2bm16c/ZcdD075x66fb93ftmOJqsazNFen3Pr0N3h5/xtEY1GeeX0p4wedycqdG5gxvBy/z08GDULurhDwmtdBxJMl0hN70esAvCYiXDXvs5zW/wwOt9RT1Ks/ACMGjqXu8C7OHDaRG877Mn0LBzKgz1Bee3OdxxFnvlh4R499bn8yzuCkJxb8//buPEquqtD3+PcknXkgAyFTAzEECAbIgFwCISEUGtQF7+pVQbj6fOBF21cK3FXP9dDHvXK9KvE9WAGhoQQxiKCUTAKleBmKEMggkJEhJOkpQ2dOutOd9FDD2e+PU4EMnfRQw66q8/uslZWuouv0r4HuX+1z9tn7mU0LytY1Ln8z6Sby/ov6llnf4pboz7hy4Y3MGH8us06fwYCyfowaNIIV9e8zedTEfEcqVOvL589O2g6RTfqhPL6COF1kyyur/8jQgSO56Kx5tLQf4Mk372H08NMYN3wCa+uWMG/69YB3LXVy+QVs3VMNesedc8ZtLJhrgoUoZRKD1ja8MXttwxuMGTBx7fQRgQND+oy40HGcnJ8OnzxqIq/c+OgRz33h7MsAiFx3b66/fDEpqdO6oCI9kbV4mwn5ctQ+65yreOSVn7J03V8ZN+JT3PrfFvC72C8wwHmnX8zk8gtwjUvfsv4MGTCc2p0fcN6ES2zHLmnGbdsPqQm2cxSLHa01579UX0P/3oN2TxsR+ODUQZPP7uX0Gms7l5TWjF3QrN0TqqyIrQMm284hApCKb1iZOBidYTtHsXJwUhOHTH333OGX9unfe5D+PdrzpfL5s5+3HSKbNCI9sVWoSKVApBI1zbYzFLP05KSLqptXc1KfUTUzRn5uy6j+5dMdxxlqO5vP6NSuz6wCdCe1FAST3NrPdoZSsT+xe+LrO/4wsbfT5+CUYZcsPnPoBWPLevUpuWUXC1ALUGM7RLapSE/M9zN3pXAYt3mC7QylJj05ac7ahjcYO2DimmkjAi3plZN8fa9uDn1YPn92yV1PVJGemK9n7krhMG7zTjBjbOcoZdtba6Zur6+hf+/Bu6aNuPxDTU7KiZI7rQs+nZHaVcFwYA8ltIu7FK9Uos7XK23lU1vqwCnLd7849+m6u0at2PPy8rZUi95QZ09J3myuEWnnVgPltkOIv7mJmlbbGfzGYMqqmlfNrGpexbC+p1TPGPnZrSf30+SkDL1lO0AuqEg7txK4ynYI8Tc3uV3701nUGN91Rmz7H84oc/ocmDJs1puThs4Yo8lJ3dYErLEdIhdUpJ1bYjuACKblDNsRBJImMXhNw6LZaxoWMXbAGWumjwy0DC4brslJXbOsfP5s13aIXFCRdm4JkMDnO26IPW5q7ybgdNs55EjbW6unbt9azYDeg3dOGxFYVz7o7Mm9nF6aEHZ8b9oOkCuabNSJYDhwEHjHdg7xLzdRW287gxxfa+rA6GW7X5j7dN1dJ6/Y88qy9lSLbpvrWEleHwWNSLtqEaCFZMUKN1GbsJ1BOudNTlp5cVXzyvTkpM/Vn9xv/HTHcYbYzlYA2oG3bYfIFY1Iu+Z12wHEv9zUrhG2M0j3eJOTnpjz7KYFzkeNf1+cchNVtjNZtqx8/uyszDx3HGea4zjT0h/f4TjO3GwcNxMakXbNUrzNaPvaDiL+YoybxLRPsp1DeiY9OWnOmoZFjBs4afW0EZe3Di4bfqHjOH773ftqFo81Lf13wZxC1+4vXVRZEYsBl9vOIf7iJrdtiDc/eZbtHJI9Pp2cdHH5/NnLMz2I4zh3Al9OP6zHm8DUB5gNDAU+j3ebzWPAKcB7xpig4ziDgaeBQUCVMeYGx3Hu6OC1DcCjwDi8xXhuMMbEO8vV6andw4fR+eI4zkmO48Qcx1nkOM6XD3t+UT5zHOVli19bfCqVqNlpO4Nk1+GTk1bufWW5DyYnNZGlCZvGmB8B84H5xpgr0k9PMsbMAZ4FAsB3gPfTz411HOd8YCxwH/BZYILjOKOP89qb0q+9DNgI3NiVXF05vWBjGD0VWGqMuT2PX7Mz/wXcaTuE+Iub0MqApcpgyjY2rZy5sWklw/uOrpox8rPbRpbm5KRF5fNnp3J4/MfSf2/Gu/x2NnBJ+trpMGA8sA74F+AGYAQw4Div/TReqQIsB77QlQAnLNLDh9GO43wTuJpjh8x3cOzweD/wVPrxXuBrQG86GDKnR5nvAOcbY650HOeW9Dc7zHGcS4GvGWN2d5Ct39HHw7uW+UXgPbw3AI8YY77YlX8RXbAa2JX+3kXywqT26v83H2iI75z02vYnJpU5fZunDJ+1+MwhM8b37lVWKotwvJbl47UCI9MfO8DBo/75euBtY8xCx3GuwivJb+Od2v0T8MZhn3v0az8AZuJd052ZftypE57a7WAY3dGQGY4dHn8acNPPLQQGc/wh80xgmTHmyvTXvBe4FXjUGDO3oxJN6+h4NcCVeNOs55HFBZKD4YABXsnW8UQ6Y0yiBZKl8stUuiBp4kPW7Ht9ztOb7j7jzZ3PrGlO7FtmjEnazpWhF7J8vFeAf3IcZwneAO5oDwNfcBxnMVABbEm/5kdALP05449z7N8AU9KvPRNvsNap7s4c62jIDMcOj1cC7zuO8zJeyf2N4w+Z3zfGHHq+Ozo63krgGuAveKPgR3pw3BN5CfjnLB9TpENucmsVcH6nnyglaVtL1dRtLVUM6D1kx/SRgY/KB551juP0Gt35KwvKO+XzZ9dl84DGmH141zqPfv7Rwx5ec9Q/Xgyce9RzHy//etRrr+tupq7cR9oKDEx/vAG4xxgzF7gdrzjh2OHxVGCJMWYeMBzvXcOhITMcOWQ+0N3QaR0dbxXeiPhVvJFptrfseRHvxmKRnHMTNQ22M4h9ranmMUt3PT/3qbq7Rq7cW3QrJz1lO0A+dKVIDx9Gr+TYIXNH6oCbHcdZCowB3qWHQ+YT6Oh4K4FNeKd4dxljsjpTIxgONOGNSkVyzk1s0frO8rH05KSL/7z5vmkv1z+6cU9b/WJjTE8HIvnytO0A+aD7SLupsiJ2HfAH2zmk9LU13LMJXC1WL8dV5vRtPnf4rFWTCnNy0ory+bM/YztEPvhtdY1seBHvdPeAzj5RpKeM29qoEpXOJE18yOp9r89Zve91xg88c/XUEZe3Di4bVigrJ/nitC5ord1uC4YDB/AmM4nkjJvcVG07gxSX+paN0/669aGLo1se3L3l4PpFxri7LEfyTZEWwruWYhQBvmo7hJSuVKKm0K99SYFqSTWPXbrrz2MdeiUmDZ2+bMqwWQP79R4wNc8xVpbPn12T569pjYq0Z/6CN9t4sO0gUprcZL0uHUhGDG6fjU0rLt7YtILhfUdvnDHyc9tH9hs3I73ubK75YpLRITq12wPBcKAViNrOISXMPTDBdgQpHQ3xnWe+tv3xOc9tusddv/+dN1JuMteXDnxzWhc0Is1EBPi67RBSekyqaTuYsbZzSOlJmPjQ1ftil63eF2P8wDNXTRtxefugsmGfyfLkpNXl82f7av9VFWnPvYS3q8FQ20GktKSStZvxdqsQyZn6lo3T61s2MrD3kO3TRl6xoXzgmec4Tq9srO3sq9Eo6NRujwXDgXbgeds5pPS4iZpW2xnEP9KTky57qu7u4av2vra0PdW6JoPDGeDJbGUrFhqRZuYR4Ju2Q0hpcZM7TrKdQfzH4PbZ0PTuJRua3mV43zEbZ4z8bE8mJ73qp9m6h2hlowxVVsTe49jFkEV6xBhj2hsXNAEqU7Guj9O36dzhs1edMXTaqb2dsoldeMlXy+fPfibnwQqMTu1m7gHbAaR0GHdPHSpRKRAJEx+6at9rlz1dd/fEt3Y+t+pAonG5MeZ4m3TvwKeXu3RqN3O/x9uzVZOOJGNuvHYb8CnbOUSOVt+yYXp9ywYGlg3dPn3EFevHD5w0xXF6jTrsU35bPn92se+d2iMakWYovWTgY51+okgXpJK1vvxFJMWjJdk0dsmu5+Y+VXf3sFV7X1saT7WuBVy8DbV9SSPS7HgA+L7tEFL8THL3ybYziHTFUZOTHrvx97+ps53JFo1IsyAYDqwDYrZzSHEzJpWA+CTbOUS6qyG+4wnbGWxSkWZPpe0AUtxMcns10M92DpFuWheKRF+2HcImFWn2PA9stR1CilcqUWN72yuRnviV7QC2qUizJBgOpIBf284hxctNbtLPoxSbBjTZUkWaZQ8DcdshpDiZVMNo2xlEuunhUCTaYjuEbSrSLAqGAzvRuzPpAWPiByB5hu0cIt2QQnNDABVpLvwcSNgOIcXFTWytRj+PUlwioUh0s+0QhUA/uFkWDAfqgEctx5Ai4yaqG21nEOmGJPAT2yEKhYo0NzQqlW5xk1v62s4g0g0LQ5GorzbvPhEVaQ4Ew4FNwELbOaR4GLfpVNsZRLqoDfip7RCFREWaOxqVSpcYt2UvuOW2c4h00YOhSFT3zB9GRZojwXBgM/Bb2zmk8LmJTbW2M4h0UTPwC9shCo2KNLd+ge4rlU6kEtUHbWcQ6aJ7QpHoHtshCo2KNIc0KpWucJPbBtrOINIF+4C7bIcoRCrS3NOoVE7MHJhgO4JIF/wyFIk22Q5RiFSkORYMB7bg4w1v5cTcVONWYJTtHCKd2A7cZztEoVKR5sd/ALrhXo7hJmo1+1GKwc9CkWir7RCFSkWaB8FwYDfw77ZzSOFxE7VttjOIdKIWnVU7IRVp/jwArLUdQgqLm9ox3HYGkU7cEYpEdU/8CahI8yS9X+kPbOeQwmGMcTFt2vFFCtn7wOO2QxQ6FWkeBcOBxcAfbeeQwmBSu2qBwbZzFLLmtnZSrms7hl8Z4LuhSFT/ATpRZjuAD/0QuBr9AvU9N1GzHfDViLQ1nuDx5aswxtC3rDfXXjj1iMffmDmD5TWbebduK9+7fCbrd+zmMxO0eqIlD4Yi0aW2QxQDjUjzLBgO1AM/s51D7Esl6nz3Tn/l5nouO+tTfOeyixjSvx/v1m094vH6HbvZ1tjERRNPZcu+/fQt6207sl9tBW6zHaJYqEjtWABssB1C7DKpPSfbzpBvsyZN4Kwx3m2zB9rjnDZy2BGPB/fvCxhSrmHDjt1MHnOKxbS+9j9DkWiz7RDFQkVqQTAciAO32M4h9hiTikNiku0cttTtaaA1nuD0kcOPeXzW6FGs276Lkwb2Z+Fb71C1S0u75tlToUj0RdshiomK1JJgOPA34AXbOcQON7mtCvDlZt4t7XH+vOoDrrnw/A4fTzttHPOmnMmAPn04Z+wprN26w2Zcv2lAdxd0m4rUru8D+22HkPxzE9W+HGYlUy6PLVvJF887mxGDBh7z+JA9zQcZOXggvXv3whhjMbHv/DAUie60HaLYqEgtSq/De7PtHJJ/bnKzL2fRvF27hfqG/by6rooHXl9G7KOqIx6v3ryNtkSCIf37MXroEP5es4WzRvvuUrItr4ci0UdshyhGjt7t2VdZEXsG+CfbOSR/2hrurYaUr259kYLWBpwXikSrbAcpRhqRFobvAjqd4hPGtDdB6lO2c4gc5qcq0Z5TkRaAYDiwB7jJdg7JDzexpRr97EnhWAP8P9shipl+mAtEMBx4Efit7RySe26iWpsjS6FIATeFItGk7SDFTEVaWG7F27JISpib3NrPdgaRtF+GItF3bIcodirSAhIMB5qB/wH4buk4PzFu02m2M4gAr6N9krNCRVpg0jvELLCdQ3LDuAd2gxlnO4f43jbgulAkmrIdpBSoSAvT/8HbB1BKTCpRV2c7g/heEvi6Fl7IHhVpAQqGA+3ANYAWjS4xbqLmoO0M4ns/CkWib9oOUUpUpAUqGA6sA76Ft7mulAg3uX2I7Qzia8+FItG7bIcoNSrSAhYMB54D7rSdQ7LIHNRCDGJLFXCD7RClSEVa+P4NeMl2CMmcm9q3GRhhO4f4Uhvw1VAkqk0yckBFWuCC4YALXA9U284imXETtfW2M4hvBUOR6BrbIUqVirQIBMOBRuBLgCaqFDE3URu3nUF8aWEoEtWqaTmkIi0SwXDgfXR9o6i5qZ3DbWcQ31kDBG2HKHUq0iISDAeeAv6v7RzSfca4KUz7JNs5xFf2410XbbUdpNSpSIvPj4FXbIeQ7jGpnTXAQNs5xDcSwLXaGi0/VKRFJhgOpICvA+ttZ5GuSyVqd9jOIL5hgG+FItH/sh3EL1SkRSgYDuwD5gGaBVok3ESd7QjiH7eEItE/2g7hJyrSIhUMBzbjlek+21mkcya1d5TtDOILPw9FovfZDuE3KtIiFgwHPgSuAlpsZ5HjMybZBglNNJJceygUid5uO4QfqUiLXDAcWAZ8BW9ygRQgN1m/ESiznUNK2rPA92yH8CsVaQkIhgN/w9sQXAvcFyA3Ud1gO4OUtEXA9aFI1LUdxK9UpCUiGA78AbjVdg45lpvY3Nt2BilZq4B/DEWi7baD+JmKtIQEw4FfAT+3nUOOZNzGcbYzSEmqAj4fikSbbAfxOxVpiQmGA7cDD9nOIR7jtu0Hd4LtHFJytgPzQpHoLttBREVaqr4HPGE7hICb3FwNOLZzSEnZjzcSrbUdRDwq0hKU3nrtvwMLbWfxu1Siutl2Bikp+4ArQ5HoWttB5BMq0hKVLtNvA7+2ncXPTLK+v+0MUjLqgdmhSPTvtoPIkRxjdMdEqausiN0L3Gw7hx+1NSzYAWaM7RxS9DYCnwtFoptsB5FjaUTqA8Fw4Bbgl7Zz+I1xm1Wikg0rgUtVooVLReoTwXDgNuA22zn8JJWo3Ww7gxS9RcDlmp1b2FSkPhIMB34JfBfQCih54CZqtAayZOJ5dJ9oUVCR+kwwHHgIuA6I285S6tzkjqG2M0jRehT4ilYsKg4qUh8KhgN/Aq4GdGtGjhhjDKblDNs5pCjdBdwYikRTtoNI12jWro9VVsSmAC8AE21nKTVuak9dvOmxCbZzSNG5LRSJamJgkdGI1MeC4cAHwD/gTWiQLHITtfW2M0hRSQE3qUSLk4rU54LhwF5gHhC2naWUpBK1Oi0nXbUf+HIoEv2N7SDSMzq1Kx+rrIgFgXvQJtQZa2u8/z1M/DzbOaTgrQG+GopEq2wHkZ7TiFQ+FgwHKoEr8dbzlB4yxk1i4pNs55CC9zvgYpVo8dOIVI5RWRGbhDcJ6RzbWYqRm9y2Pt785Nm2c0jBagduDkWi2u6wRGhEKscIhgNVwEzgr7azFKNUokar0Mjx1AGzVKKlRUUqHQqGA01495r+J96MQukiN1Gn/UelIy8BF4Qi0RW2g0h26dSudKqyInYp3kbhp9nOUgzaGn61AZJn2c4hBcMF/gP4z1Akql+4JUhFKl1SWREbhneLzLW2sxQyYxIt7Y339QN6284iBWEvcH0oEn3ZdhDJHRWpdEtlRexbwP3AYNtZClEqUbs2ceC5823nkILwNvC1UCSqXYBKnK6RSrcEw4HfAdPwfknIUdx4dYPtDGKdi3c/9myVqD9oRCo9UlkRKwPuAH6E3pB9rH3/wqXGbbjEdg6x5kPg26FIdLntIJI/KlLJSGVFbA7wOHCq7SyFoK3hns3galKW/8SBXwB3hiJRbVHoMypSyVh6ItJdwI2Ab2/9MG7Lvvb94RG2c0jeLQP+JRSJfmg7iNihIpWsqayIzcKb2Xuu7Sw2pOLrViQOvnSB7RySNweAHwOVoUjUtR1G7FGRSlalr53+K/ATYJDlOHkVP/CXRW5i/VzbOSQvXgIqNJlIQEUqOVJZETsN+BXwj7az5Etb48NvY5r/wXYOyak9wK2hSPQJ20GkcKhIJacqK2JXA/cBp9vOkmttDQt2gTnFdg7JmSfwSnSP7SBSWFSkknOVFbGBeKd6/xXoYzlOTrip/dviTY+Ms51DcqIG+EEoEtUmDtIhFankTWVFbApwL3CF7SzZlmxbvTzZGptpO4dk1R68TRseDEWiCdthpHCpSCXvKitiVwA/By6ynSVb4s3PLXKTtXNt55CsaMVbmWh+KBJtsh1GCp+KVKyprIh9Ce8df9HfLtPW+OBqTOs02zkkIy7wKPDvoUi03nIWKSIqUrGqsiLWC7geb5upiZbj9IgxxrQ3LmgGhtrOIj1igOfwCvQD22Gk+KhIpSBUVsT6AN8G/g0oqkk7bnJXTbz58aJ8EyC8APwkFImuth1EipeKVApKZUVsAPB94H8DIy3H6ZJk69/fSrYtudR2DumWv+KNQFfYDiLFT0UqBamyIjYUuAn4AQV+D2p785OLTXLbHNs5pFMG+BvwU+3OItmkIpWCVlkR6w18GbgVmGU5TofaGu7/AOJTbOeQ42rEm0T0QCgS3Wg5i5QgFakUjcqK2IV4hfo1CmRhB2NS8fbGew3Qz3YWOcZaoBJ4IhSJHrQdRkqXilSKTmVFbDwQBL6D5euobmLrh/EDf/q0zQxyhCTeDNz7Q5HoYtthxB9UpFK00hOTvok3Sj3HRoZEyxuLU+0rdH3Uvh3AQ8CvQ5HoNtthxF9UpFISKitilwDfAK4hj6PU9qbfv2VSuzVj154lwP3AM1rGT2xRkUpJSd+P+nm8Ur0aGJDLr9fWcG8VpCbl8mvIMXYAzwIP6/5PKQQqUilZ6VtovgL8M3A50CubxzcmfqC98f6B2T6udGgTXnk+AywLRaKu5TwiH1ORii9UVsTG4S1F+A1gajaOmYpXr04cfF7r6+bOR3jl+awWTpBCpiIV36msiJ2Jd/r3C8Bcenj6N3Hw5TdS8fcvy2I0gdV4o85nQ5Hoh7bD9JTjOCfhzR7uBdxrjHmuo+dsZpTsUZGKr1VWxPoDl+EV6+eByV19bfv+3y4zbuPFucrmEwZYzicjzxrLebLCcZw5wDxjzO0nek5Kg4pU5DCVFbEJfFKqVwCDj/e5bQ331IM7Pk/RSkUSb9S5JP3nzVAkusNupK5zHKc/3ipJ5XgrJl0P/AkYBFQZY25wHOcW4AZgGFCHt4DI9R08dxB4DDgFeM8YE8zjtyJZpCIVOY70DOBZwBxgZvrPcADjHtzTvv/XJ1uMVywagGXAUrzifDsUibbYjdRzjuPcDAwyxtzpOM4NwIfAycCreOv4ft0Ys9NxnLnAXGPMHYe99ojnHMe5FRhmjLnDcZxngTuMMWvz+f1IdpTZDiBSqILhQAJYlP5DZUXMAc4GZrrJ+rOAeXibkmt5wE9U8clocynwYSgSLaV365PxruGCNzKdANyGN9ocQfeut58NXJIu2GHAeLxlDaXIqEhFuigYDhi8maQfQQCo+PHd117VB/g0MB2Ykf57Gic4JVwi9uOVZhWwEVgBLA1Foruspsq9j4ALgdeAH+Nt+fe/8E7pMAOgAAAB+klEQVTvvtHNY60H3jbGLHQc5ypgczaDSv7o1K5Ilt197VW9gFPxtn877ai/D3080FrArtvDJ2VZBVQf+jgUie6xGcwWx3EGAL/Du665F7gXeADvFHZv4IfGmCVdPLU7CFgIjAGagOuNMU35+l4ke1SkIhbcfe1VJ3NsuR76+xSgP95pwv5kb6ebg3i/sJuA5sM+PvRnB4cVZygS3Z+lrytS0lSkIgUuPcI9VKr9O/nY5diSbAaaQ5FoKu/hRXxARSoiIpIBrREqIiKSARWpiIhIBlSkIiIiGVCRioiIZEBFKiIikgEVqYiISAZUpCIiIhlQkYqIiGRARSoiIpIBFamIiEgGVKQiIiIZUJGKiIhkQEUqIiKSARWpiIhIBlSkIiIiGVCRioiIZEBFKiIikgEVqYiISAZUpCIiIhlQkYqIiGRARSoiIpIBFamIiEgGVKQiIiIZUJGKiIhkQEUqIiKSARWpiIhIBlSkIiIiGVCRioiIZEBFKiIikgEVqYiISAZUpCIiIhlQkYqIiGRARSoiIpIBFamIiEgGVKQiIiIZUJGKiIhkQEUqIiKSARWpiIhIBlSkIiIiGVCRioiIZEBFKiIikgEVqYiISAZUpCIiIhlQkYqIiGRARSoiIpIBFamIiEgGVKQiIiIZUJGKiIhkQEUqIiKSgf8PSElRPQ8BrYMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "other=1-data_pl['percentage'].values.sum()\n",
    "head_counts=list(data_pl['percentage'].values)\n",
    "head_counts.append(other)\n",
    "head_counts\n",
    "\n",
    "labels=list(data_pl['ml_'].values)\n",
    "labels.append('其他')\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(head_counts, labels=labels, autopct='%1.0f%%')\n",
    "plt.title(f'算法工程师的机器学习库')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 英语"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_=data.shape[0]\n",
    "en=data[data['english']==1].shape[0]\n",
    "other=all_-en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie([en,other], labels=['要求英语','没说'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_counts=data[~(data.edu=='')].groupby(by=['edu']).headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu\n",
       "博士     119\n",
       "大专     260\n",
       "本科    1325\n",
       "硕士     741\n",
       "高中       2\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(edu_counts.values, labels=edu_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >edu</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow0_col1\" class=\"data row0 col1\" >27184</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow0_col2\" class=\"data row0 col2\" >17469</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow0_col3\" class=\"data row0 col3\" >27500</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow0_col4\" class=\"data row0 col4\" >54263</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow0_col5\" class=\"data row0 col5\" >119</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow0_col6\" class=\"data row0 col6\" >4.86%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow1_col1\" class=\"data row1 col1\" >25217</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow1_col2\" class=\"data row1 col2\" >10000</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow1_col4\" class=\"data row1 col4\" >53967</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow1_col5\" class=\"data row1 col5\" >741</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow1_col6\" class=\"data row1 col6\" >30.28%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow2_col1\" class=\"data row2 col1\" >24000</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow2_col4\" class=\"data row2 col4\" >48437</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow2_col5\" class=\"data row2 col5\" >1325</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow2_col6\" class=\"data row2 col6\" >54.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow3_col1\" class=\"data row3 col1\" >9830</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow3_col2\" class=\"data row3 col2\" >3805</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow3_col3\" class=\"data row3 col3\" >7000</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow3_col4\" class=\"data row3 col4\" >26786</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow3_col5\" class=\"data row3 col5\" >260</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow3_col6\" class=\"data row3 col6\" >10.63%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow4_col0\" class=\"data row4 col0\" >高中</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow4_col1\" class=\"data row4 col1\" >5875</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow4_col2\" class=\"data row4 col2\" >4750</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow4_col3\" class=\"data row4 col3\" >5875</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow4_col4\" class=\"data row4 col4\" >7000</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow4_col5\" class=\"data row4 col5\" >2</td>\n",
       "                        <td id=\"T_f4a1d31e_5cc0_11ea_9d8d_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.08%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19234920f08>"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['初中及以下','高中','大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是11406，23224，24403，30006。中位数分别是9000， 21500，22500， 27500。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=9.30965197268674, pvalue=0.002502240773058869)"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1.7137682733121973, pvalue=0.19093937936927335)"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=59.4378531195539, pvalue=6.800647889450831e-14)"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >experience</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow0_col0\" class=\"data row0 col0\" >10+</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow0_col1\" class=\"data row0 col1\" >36000</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow0_col2\" class=\"data row0 col2\" >30000</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow0_col3\" class=\"data row0 col3\" >36250</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow0_col4\" class=\"data row0 col4\" >40000</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow0_col5\" class=\"data row0 col5\" >5</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.18%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow1_col1\" class=\"data row1 col1\" >31819</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow1_col2\" class=\"data row1 col2\" >6843</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow1_col3\" class=\"data row1 col3\" >30000</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow1_col4\" class=\"data row1 col4\" >60000</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow1_col5\" class=\"data row1 col5\" >219</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow1_col6\" class=\"data row1 col6\" >7.85%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow2_col1\" class=\"data row2 col1\" >26836</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow2_col2\" class=\"data row2 col2\" >11500</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow2_col3\" class=\"data row2 col3\" >25000</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow2_col4\" class=\"data row2 col4\" >49875</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow2_col5\" class=\"data row2 col5\" >662</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow2_col6\" class=\"data row2 col6\" >23.73%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow3_col0\" class=\"data row3 col0\" >1_3</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow3_col1\" class=\"data row3 col1\" >22743</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow3_col2\" class=\"data row3 col2\" >7469</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow3_col3\" class=\"data row3 col3\" >20833</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow3_col4\" class=\"data row3 col4\" >40966</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow3_col5\" class=\"data row3 col5\" >795</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow3_col6\" class=\"data row3 col6\" >28.49%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow4_col0\" class=\"data row4 col0\" ></td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow4_col1\" class=\"data row4 col1\" >18110</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow4_col2\" class=\"data row4 col2\" >4009</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow4_col3\" class=\"data row4 col3\" >14940</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow4_col4\" class=\"data row4 col4\" >49510</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow4_col5\" class=\"data row4 col5\" >1109</td>\n",
       "                        <td id=\"T_ff16c28c_5cc0_11ea_909a_701ce71031efrow4_col6\" class=\"data row4 col6\" >39.75%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x192349c8888>"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_type</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow0_col0\" class=\"data row0 col0\" >非营利组织</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow0_col1\" class=\"data row0 col1\" >26625</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow0_col2\" class=\"data row0 col2\" >11500</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow0_col3\" class=\"data row0 col3\" >27357</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow0_col4\" class=\"data row0 col4\" >40000</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow0_col5\" class=\"data row0 col5\" >8</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.29%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow1_col0\" class=\"data row1 col0\" >外资（欧美）</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow1_col1\" class=\"data row1 col1\" >25986</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow1_col2\" class=\"data row1 col2\" >4975</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow1_col4\" class=\"data row1 col4\" >60000</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow1_col5\" class=\"data row1 col5\" >169</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow1_col6\" class=\"data row1 col6\" >6.06%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow2_col0\" class=\"data row2 col0\" >外资（非欧美）</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow2_col1\" class=\"data row2 col1\" >25198</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow2_col2\" class=\"data row2 col2\" >12500</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow2_col4\" class=\"data row2 col4\" >47708</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow2_col5\" class=\"data row2 col5\" >126</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow2_col6\" class=\"data row2 col6\" >4.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow3_col0\" class=\"data row3 col0\" >合资</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow3_col1\" class=\"data row3 col1\" >23408</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow3_col2\" class=\"data row3 col2\" >5597</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow3_col3\" class=\"data row3 col3\" >22500</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow3_col4\" class=\"data row3 col4\" >47232</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow3_col5\" class=\"data row3 col5\" >331</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow3_col6\" class=\"data row3 col6\" >11.86%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow4_col0\" class=\"data row4 col0\" >民营公司</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow4_col1\" class=\"data row4 col1\" >22599</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow4_col2\" class=\"data row4 col2\" >5250</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow4_col3\" class=\"data row4 col3\" >20000</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow4_col4\" class=\"data row4 col4\" >53667</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow4_col5\" class=\"data row4 col5\" >1830</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow4_col6\" class=\"data row4 col6\" >65.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow5_col0\" class=\"data row5 col0\" >国企</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow5_col1\" class=\"data row5 col1\" >19473</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow5_col2\" class=\"data row5 col2\" >6480</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow5_col3\" class=\"data row5 col3\" >17500</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow5_col4\" class=\"data row5 col4\" >32266</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow5_col5\" class=\"data row5 col5\" >274</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow5_col6\" class=\"data row5 col6\" >9.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow6_col0\" class=\"data row6 col0\" >事业单位</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow6_col1\" class=\"data row6 col1\" >16498</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow6_col2\" class=\"data row6 col2\" >6543</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow6_col3\" class=\"data row6 col3\" >11500</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow6_col4\" class=\"data row6 col4\" >35000</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow6_col5\" class=\"data row6 col5\" >52</td>\n",
       "                        <td id=\"T_04e4c9c2_5cc1_11ea_b103_701ce71031efrow6_col6\" class=\"data row6 col6\" >1.86%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x192348c7408>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_size</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000+</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow0_col1\" class=\"data row0 col1\" >35437</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow0_col2\" class=\"data row0 col2\" >15900</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow0_col3\" class=\"data row0 col3\" >35000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow0_col4\" class=\"data row0 col4\" >57842</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow0_col5\" class=\"data row0 col5\" >354</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow0_col6\" class=\"data row0 col6\" >12.69%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow1_col0\" class=\"data row1 col0\" >500-1000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow1_col1\" class=\"data row1 col1\" >22497</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow1_col2\" class=\"data row1 col2\" >5596</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow1_col4\" class=\"data row1 col4\" >60075</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow1_col5\" class=\"data row1 col5\" >297</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow1_col6\" class=\"data row1 col6\" >10.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow2_col0\" class=\"data row2 col0\" >1000-5000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow2_col1\" class=\"data row2 col1\" >21880</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow2_col2\" class=\"data row2 col2\" >7279</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow2_col3\" class=\"data row2 col3\" >20833</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow2_col4\" class=\"data row2 col4\" >45000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow2_col5\" class=\"data row2 col5\" >438</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow2_col6\" class=\"data row2 col6\" >15.70%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow3_col0\" class=\"data row3 col0\" >150-500</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow3_col1\" class=\"data row3 col1\" >21742</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow3_col2\" class=\"data row3 col2\" >6650</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow3_col3\" class=\"data row3 col3\" >20000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow3_col4\" class=\"data row3 col4\" >50625</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow3_col5\" class=\"data row3 col5\" >639</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow3_col6\" class=\"data row3 col6\" >22.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow4_col0\" class=\"data row4 col0\" >5000-10000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow4_col1\" class=\"data row4 col1\" >21673</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow4_col2\" class=\"data row4 col2\" >5250</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow4_col4\" class=\"data row4 col4\" >39344</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow4_col5\" class=\"data row4 col5\" >61</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow4_col6\" class=\"data row4 col6\" >2.19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow5_col0\" class=\"data row5 col0\" >50-</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow5_col1\" class=\"data row5 col1\" >20434</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow5_col2\" class=\"data row5 col2\" >4650</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow5_col3\" class=\"data row5 col3\" >15500</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow5_col4\" class=\"data row5 col4\" >61643</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow5_col5\" class=\"data row5 col5\" >348</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow5_col6\" class=\"data row5 col6\" >12.47%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow6_col0\" class=\"data row6 col0\" >50-150</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow6_col1\" class=\"data row6 col1\" >18423</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow6_col2\" class=\"data row6 col2\" >5081</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow6_col4\" class=\"data row6 col4\" >40000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow6_col5\" class=\"data row6 col5\" >638</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow6_col6\" class=\"data row6 col6\" >22.87%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow7_col0\" class=\"data row7 col0\" ></td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow7_col1\" class=\"data row7 col1\" >12517</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow7_col2\" class=\"data row7 col2\" >3750</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow7_col3\" class=\"data row7 col3\" >8000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow7_col4\" class=\"data row7 col4\" >55000</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow7_col5\" class=\"data row7 col5\" >15</td>\n",
       "                        <td id=\"T_05e18368_5cc1_11ea_9ecd_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.54%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1923498b248>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >industry</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow0_col0\" class=\"data row0 col0\" >logistic</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow0_col1\" class=\"data row0 col1\" >32963</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow0_col2\" class=\"data row0 col2\" >8225</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow0_col3\" class=\"data row0 col3\" >36000</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow0_col4\" class=\"data row0 col4\" >54000</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow0_col5\" class=\"data row0 col5\" >47</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow0_col6\" class=\"data row0 col6\" >1.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow1_col0\" class=\"data row1 col0\" >finance</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow1_col1\" class=\"data row1 col1\" >27314</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow1_col2\" class=\"data row1 col2\" >6425</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow1_col3\" class=\"data row1 col3\" >26429</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow1_col4\" class=\"data row1 col4\" >71296</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow1_col5\" class=\"data row1 col5\" >117</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow1_col6\" class=\"data row1 col6\" >4.19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow2_col0\" class=\"data row2 col0\" >computer</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow2_col1\" class=\"data row2 col1\" >23206</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow2_col2\" class=\"data row2 col2\" >5250</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow2_col3\" class=\"data row2 col3\" >22500</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow2_col4\" class=\"data row2 col4\" >48250</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow2_col5\" class=\"data row2 col5\" >2080</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow2_col6\" class=\"data row2 col6\" >74.55%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow3_col0\" class=\"data row3 col0\" >edu</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow3_col1\" class=\"data row3 col1\" >22672</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow3_col2\" class=\"data row3 col2\" >5250</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow3_col3\" class=\"data row3 col3\" >20000</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow3_col4\" class=\"data row3 col4\" >61143</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow3_col5\" class=\"data row3 col5\" >176</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow3_col6\" class=\"data row3 col6\" >6.31%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow4_col0\" class=\"data row4 col0\" >gov</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow4_col1\" class=\"data row4 col1\" >22320</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow4_col2\" class=\"data row4 col2\" >11500</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow4_col3\" class=\"data row4 col3\" >18333</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow4_col4\" class=\"data row4 col4\" >37500</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow4_col5\" class=\"data row4 col5\" >25</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow5_col0\" class=\"data row5 col0\" >medical</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow5_col1\" class=\"data row5 col1\" >19405</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow5_col2\" class=\"data row5 col2\" >5533</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow5_col3\" class=\"data row5 col3\" >17500</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow5_col4\" class=\"data row5 col4\" >50333</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow5_col5\" class=\"data row5 col5\" >56</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow5_col6\" class=\"data row5 col6\" >2.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow6_col0\" class=\"data row6 col0\" >energy</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow6_col1\" class=\"data row6 col1\" >19385</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow6_col2\" class=\"data row6 col2\" >10416</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow6_col3\" class=\"data row6 col3\" >17917</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow6_col4\" class=\"data row6 col4\" >37500</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow6_col5\" class=\"data row6 col5\" >28</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow6_col6\" class=\"data row6 col6\" >1.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow7_col0\" class=\"data row7 col0\" >trade</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow7_col1\" class=\"data row7 col1\" >16365</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow7_col2\" class=\"data row7 col2\" >6446</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow7_col3\" class=\"data row7 col3\" >12500</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow7_col4\" class=\"data row7 col4\" >37500</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow7_col5\" class=\"data row7 col5\" >165</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow7_col6\" class=\"data row7 col6\" >5.91%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow8_col0\" class=\"data row8 col0\" >service</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow8_col1\" class=\"data row8 col1\" >13182</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow8_col2\" class=\"data row8 col2\" >12500</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow8_col3\" class=\"data row8 col3\" >12812</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow8_col4\" class=\"data row8 col4\" >25937</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow8_col5\" class=\"data row8 col5\" >33</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow8_col6\" class=\"data row8 col6\" >1.18%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow9_col0\" class=\"data row9 col0\" >ads</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow9_col1\" class=\"data row9 col1\" >12244</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow9_col2\" class=\"data row9 col2\" >7000</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow9_col3\" class=\"data row9 col3\" >7000</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow9_col4\" class=\"data row9 col4\" >38750</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow9_col5\" class=\"data row9 col5\" >45</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow9_col6\" class=\"data row9 col6\" >1.61%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow10_col0\" class=\"data row10 col0\" >realestate</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow10_col1\" class=\"data row10 col1\" >11111</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow10_col2\" class=\"data row10 col2\" >7000</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow10_col3\" class=\"data row10 col3\" >8438</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow10_col4\" class=\"data row10 col4\" >25000</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow10_col5\" class=\"data row10 col5\" >18</td>\n",
       "                        <td id=\"T_06fe32a2_5cc1_11ea_9af0_701ce71031efrow10_col6\" class=\"data row10 col6\" >0.65%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x192347f29c8>"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
